{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised Learning (1) - Linear Regression\n",
    "## Machine Learning Lectures by the ESA Data Analytics Team for Operations (DATO)\n",
    "#### [José Martínez Heras](https://www.linkedin.com/in/josemartinezheras/)\n",
    "\n",
    "## Resouces\n",
    "This notebook is best followed when watched along to its corresponding [linear regression with Frankfurt Airbnb data **video**](https://dlmultimedia.esa.int/download/public/videos/2048/03/003/4803_003_AR_EN.mp4)\n",
    "\n",
    "The tutorial about Linear Regression can be found in the [2018-MachineLearning-Lectures-ESA **GitHub**](https://github.com/jmartinezheras/2018-MachineLearning-Lectures-ESA)\n",
    "\n",
    "## Goal of today's project\n",
    "Predict how much it will cost a overnight stay in Frankfurt at Airbnb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, RidgeCV, LassoCV, ElasticNetCV\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import random\n",
    "\n",
    "#Let's make this notebook reproducible \n",
    "np.random.seed(42)\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the Airbnb data here http://tomslee.net/airbnb-data-collection-get-the-data\n",
    "\n",
    "In particular, the Frankfurt data can be downloaded at: https://s3.amazonaws.com/tomslee-airbnb-data-2/frankfurt.zip\n",
    "\n",
    "We will be using only the most recent data from 22.06.2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frankfurt = pd.read_csv('datasets/tomslee_airbnb_frankfurt_1360_2017-06-22.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>survey_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>room_type</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>borough</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>minstay</th>\n",
       "      <th>last_modified</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14816898</td>\n",
       "      <td>1360</td>\n",
       "      <td>92278018</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahnhofsviertel</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-22 15:39:57.993776</td>\n",
       "      <td>50.108833</td>\n",
       "      <td>8.668395</td>\n",
       "      <td>0101000020E6100000AE9E93DE37562140DFDC5F3DEE0D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15294143</td>\n",
       "      <td>1360</td>\n",
       "      <td>62507234</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Westend-Süd</td>\n",
       "      <td>27</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-22 15:39:56.482515</td>\n",
       "      <td>50.120212</td>\n",
       "      <td>8.657187</td>\n",
       "      <td>0101000020E610000074B680D07A5021401B4B581B630F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6751387</td>\n",
       "      <td>1360</td>\n",
       "      <td>17826701</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rödelheim</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-22 15:39:54.515975</td>\n",
       "      <td>50.128278</td>\n",
       "      <td>8.596030</td>\n",
       "      <td>0101000020E6100000A9DE1AD82A312140ED65DB696B10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15637480</td>\n",
       "      <td>1360</td>\n",
       "      <td>85072001</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oberrad</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>346.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-22 15:39:52.319758</td>\n",
       "      <td>50.099801</td>\n",
       "      <td>8.722627</td>\n",
       "      <td>0101000020E6100000C669882AFC712140D68D7747C60C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18244045</td>\n",
       "      <td>1360</td>\n",
       "      <td>68408403</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Innenstadt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-22 15:39:47.234792</td>\n",
       "      <td>50.115677</td>\n",
       "      <td>8.694514</td>\n",
       "      <td>0101000020E6100000713AC956976321401FF30181CE0E...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    room_id  survey_id   host_id    room_type  country       city  borough  \\\n",
       "0  14816898       1360  92278018  Shared room      NaN  Frankfurt      NaN   \n",
       "1  15294143       1360  62507234  Shared room      NaN  Frankfurt      NaN   \n",
       "2   6751387       1360  17826701  Shared room      NaN  Frankfurt      NaN   \n",
       "3  15637480       1360  85072001  Shared room      NaN  Frankfurt      NaN   \n",
       "4  18244045       1360  68408403  Shared room      NaN  Frankfurt      NaN   \n",
       "\n",
       "      neighborhood  reviews  overall_satisfaction  accommodates  bedrooms  \\\n",
       "0  Bahnhofsviertel       51                   4.5            10       1.0   \n",
       "1      Westend-Süd       27                   4.5             2       1.0   \n",
       "2        Rödelheim        0                   0.0             2       1.0   \n",
       "3          Oberrad        0                   0.0             6       1.0   \n",
       "4       Innenstadt        0                   0.0             2       1.0   \n",
       "\n",
       "   bathrooms  price  minstay               last_modified   latitude  \\\n",
       "0        NaN   80.0      NaN  2017-06-22 15:39:57.993776  50.108833   \n",
       "1        NaN   52.0      NaN  2017-06-22 15:39:56.482515  50.120212   \n",
       "2        NaN   35.0      NaN  2017-06-22 15:39:54.515975  50.128278   \n",
       "3        NaN  346.0      NaN  2017-06-22 15:39:52.319758  50.099801   \n",
       "4        NaN  149.0      NaN  2017-06-22 15:39:47.234792  50.115677   \n",
       "\n",
       "   longitude                                           location  \n",
       "0   8.668395  0101000020E6100000AE9E93DE37562140DFDC5F3DEE0D...  \n",
       "1   8.657187  0101000020E610000074B680D07A5021401B4B581B630F...  \n",
       "2   8.596030  0101000020E6100000A9DE1AD82A312140ED65DB696B10...  \n",
       "3   8.722627  0101000020E6100000C669882AFC712140D68D7747C60C...  \n",
       "4   8.694514  0101000020E6100000713AC956976321401FF30181CE0E...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find which fields don't contain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>survey_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>country</th>\n",
       "      <th>borough</th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>minstay</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.995000e+03</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1.995000e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.167641e+07</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>4.092822e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.171429</td>\n",
       "      <td>2.544110</td>\n",
       "      <td>2.263659</td>\n",
       "      <td>1.099248</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.532832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.117200</td>\n",
       "      <td>8.666842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.467073e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.603036e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.459738</td>\n",
       "      <td>2.368573</td>\n",
       "      <td>1.315237</td>\n",
       "      <td>0.542270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.226681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018407</td>\n",
       "      <td>0.035737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.626700e+04</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1.659300e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.024792</td>\n",
       "      <td>8.489325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.665019e+06</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1.032169e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.105532</td>\n",
       "      <td>8.645188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.255222e+07</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>2.991108e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.116671</td>\n",
       "      <td>8.670427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.658699e+07</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>6.171958e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.124712</td>\n",
       "      <td>8.691819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.942335e+07</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1.360651e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>864.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.206080</td>\n",
       "      <td>8.772625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            room_id  survey_id       host_id  country  borough      reviews  \\\n",
       "count  1.995000e+03     1995.0  1.995000e+03      0.0      0.0  1995.000000   \n",
       "mean   1.167641e+07     1360.0  4.092822e+07      NaN      NaN    11.171429   \n",
       "std    5.467073e+06        0.0  3.603036e+07      NaN      NaN    25.459738   \n",
       "min    6.626700e+04     1360.0  1.659300e+04      NaN      NaN     0.000000   \n",
       "25%    7.665019e+06     1360.0  1.032169e+07      NaN      NaN     1.000000   \n",
       "50%    1.255222e+07     1360.0  2.991108e+07      NaN      NaN     3.000000   \n",
       "75%    1.658699e+07     1360.0  6.171958e+07      NaN      NaN    10.000000   \n",
       "max    1.942335e+07     1360.0  1.360651e+08      NaN      NaN   465.000000   \n",
       "\n",
       "       overall_satisfaction  accommodates     bedrooms  bathrooms  \\\n",
       "count           1995.000000   1995.000000  1995.000000        0.0   \n",
       "mean               2.544110      2.263659     1.099248        NaN   \n",
       "std                2.368573      1.315237     0.542270        NaN   \n",
       "min                0.000000      1.000000     0.000000        NaN   \n",
       "25%                0.000000      2.000000     1.000000        NaN   \n",
       "50%                4.000000      2.000000     1.000000        NaN   \n",
       "75%                5.000000      2.000000     1.000000        NaN   \n",
       "max                5.000000     12.000000     8.000000        NaN   \n",
       "\n",
       "             price  minstay     latitude    longitude  \n",
       "count  1995.000000      0.0  1995.000000  1995.000000  \n",
       "mean     77.532832      NaN    50.117200     8.666842  \n",
       "std      59.226681      NaN     0.018407     0.035737  \n",
       "min      10.000000      NaN    50.024792     8.489325  \n",
       "25%      41.000000      NaN    50.105532     8.645188  \n",
       "50%      58.000000      NaN    50.116671     8.670427  \n",
       "75%      92.000000      NaN    50.124712     8.691819  \n",
       "max     864.000000      NaN    50.206080     8.772625  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### drop fields for which we have no data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>survey_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>room_type</th>\n",
       "      <th>city</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14816898</td>\n",
       "      <td>1360</td>\n",
       "      <td>92278018</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Bahnhofsviertel</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.108833</td>\n",
       "      <td>8.668395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15294143</td>\n",
       "      <td>1360</td>\n",
       "      <td>62507234</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Westend-Süd</td>\n",
       "      <td>27</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.120212</td>\n",
       "      <td>8.657187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6751387</td>\n",
       "      <td>1360</td>\n",
       "      <td>17826701</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Rödelheim</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.128278</td>\n",
       "      <td>8.596030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15637480</td>\n",
       "      <td>1360</td>\n",
       "      <td>85072001</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Oberrad</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>50.099801</td>\n",
       "      <td>8.722627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18244045</td>\n",
       "      <td>1360</td>\n",
       "      <td>68408403</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Innenstadt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>50.115677</td>\n",
       "      <td>8.694514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    room_id  survey_id   host_id    room_type       city     neighborhood  \\\n",
       "0  14816898       1360  92278018  Shared room  Frankfurt  Bahnhofsviertel   \n",
       "1  15294143       1360  62507234  Shared room  Frankfurt      Westend-Süd   \n",
       "2   6751387       1360  17826701  Shared room  Frankfurt        Rödelheim   \n",
       "3  15637480       1360  85072001  Shared room  Frankfurt          Oberrad   \n",
       "4  18244045       1360  68408403  Shared room  Frankfurt       Innenstadt   \n",
       "\n",
       "   reviews  overall_satisfaction  accommodates  bedrooms  price   latitude  \\\n",
       "0       51                   4.5            10       1.0   80.0  50.108833   \n",
       "1       27                   4.5             2       1.0   52.0  50.120212   \n",
       "2        0                   0.0             2       1.0   35.0  50.128278   \n",
       "3        0                   0.0             6       1.0  346.0  50.099801   \n",
       "4        0                   0.0             2       1.0  149.0  50.115677   \n",
       "\n",
       "   longitude  \n",
       "0   8.668395  \n",
       "1   8.657187  \n",
       "2   8.596030  \n",
       "3   8.722627  \n",
       "4   8.694514  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt = frankfurt.drop(['country', 'last_modified', 'location', 'borough', 'bathrooms', 'minstay'], axis=1)\n",
    "frankfurt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### drop \"irrelavant\" information for today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Bahnhofsviertel</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.108833</td>\n",
       "      <td>8.668395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Westend-Süd</td>\n",
       "      <td>27</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.120212</td>\n",
       "      <td>8.657187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Rödelheim</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.128278</td>\n",
       "      <td>8.596030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Oberrad</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>50.099801</td>\n",
       "      <td>8.722627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Innenstadt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>50.115677</td>\n",
       "      <td>8.694514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     room_type     neighborhood  reviews  overall_satisfaction  accommodates  \\\n",
       "0  Shared room  Bahnhofsviertel       51                   4.5            10   \n",
       "1  Shared room      Westend-Süd       27                   4.5             2   \n",
       "2  Shared room        Rödelheim        0                   0.0             2   \n",
       "3  Shared room          Oberrad        0                   0.0             6   \n",
       "4  Shared room       Innenstadt        0                   0.0             2   \n",
       "\n",
       "   bedrooms  price   latitude  longitude  \n",
       "0       1.0   80.0  50.108833   8.668395  \n",
       "1       1.0   52.0  50.120212   8.657187  \n",
       "2       1.0   35.0  50.128278   8.596030  \n",
       "3       1.0  346.0  50.099801   8.722627  \n",
       "4       1.0  149.0  50.115677   8.694514  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt = frankfurt.drop(['room_id', 'survey_id','host_id', 'city'], axis=1)\n",
    "frankfurt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.171429</td>\n",
       "      <td>2.544110</td>\n",
       "      <td>2.263659</td>\n",
       "      <td>1.099248</td>\n",
       "      <td>77.532832</td>\n",
       "      <td>50.117200</td>\n",
       "      <td>8.666842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.459738</td>\n",
       "      <td>2.368573</td>\n",
       "      <td>1.315237</td>\n",
       "      <td>0.542270</td>\n",
       "      <td>59.226681</td>\n",
       "      <td>0.018407</td>\n",
       "      <td>0.035737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>50.024792</td>\n",
       "      <td>8.489325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>50.105532</td>\n",
       "      <td>8.645188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>50.116671</td>\n",
       "      <td>8.670427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>50.124712</td>\n",
       "      <td>8.691819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>465.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>864.000000</td>\n",
       "      <td>50.206080</td>\n",
       "      <td>8.772625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           reviews  overall_satisfaction  accommodates     bedrooms  \\\n",
       "count  1995.000000           1995.000000   1995.000000  1995.000000   \n",
       "mean     11.171429              2.544110      2.263659     1.099248   \n",
       "std      25.459738              2.368573      1.315237     0.542270   \n",
       "min       0.000000              0.000000      1.000000     0.000000   \n",
       "25%       1.000000              0.000000      2.000000     1.000000   \n",
       "50%       3.000000              4.000000      2.000000     1.000000   \n",
       "75%      10.000000              5.000000      2.000000     1.000000   \n",
       "max     465.000000              5.000000     12.000000     8.000000   \n",
       "\n",
       "             price     latitude    longitude  \n",
       "count  1995.000000  1995.000000  1995.000000  \n",
       "mean     77.532832    50.117200     8.666842  \n",
       "std      59.226681     0.018407     0.035737  \n",
       "min      10.000000    50.024792     8.489325  \n",
       "25%      41.000000    50.105532     8.645188  \n",
       "50%      58.000000    50.116671     8.670427  \n",
       "75%      92.000000    50.124712     8.691819  \n",
       "max     864.000000    50.206080     8.772625  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that there are no missing values (in case we would need to handle them)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_type               0\n",
       "neighborhood            0\n",
       "reviews                 0\n",
       "overall_satisfaction    0\n",
       "accommodates            0\n",
       "bedrooms                0\n",
       "price                   0\n",
       "latitude                0\n",
       "longitude               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cool, no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory Data Analysis helps us improving our problem understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x228688f6b00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286c3f0ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sb.distplot(frankfurt['price'], rug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect how the fields affect the price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look to the categorical fields: room_type and neighborhood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many different room types there are?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Shared room', 'Entire home/apt', 'Private room'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt['room_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which neighborhoods there are in Frankfurt Airbnb?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Altstadt', 'Bahnhofsviertel', 'Bergen-Enkheim', 'Bockenheim', 'Bonames', 'Bornheim', 'Dornbusch', 'Eckenheim', 'Eschersheim', 'Fechenheim', 'Flughafen', 'Frankfurter Berg', 'Gallus', 'Ginnheim', 'Griesheim', 'Gutleutviertel', 'Harheim', 'Hausen', 'Heddernheim', 'Höchst', 'Innenstadt', 'Kalbach-Riedberg', 'Nied', 'Nieder-Erlenbach', 'Nieder-Eschbach', 'Niederrad', 'Niederursel', 'Nordend-Ost', 'Nordend-West', 'Oberrad', 'Ostend', 'Praunheim', 'Preungesheim', 'Riederwald', 'Rödelheim', 'Sachsenhausen-N.', 'Sachsenhausen-S.', 'Schwanheim', 'Seckbach', 'Sindlingen', 'Sossenheim', 'Unterliederbach', 'Westend-Nord', 'Westend-Süd', 'Zeilsheim']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(frankfurt['neighborhood'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which room_type has higher prices?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2286c489ba8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286c57c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.boxplot(column='price', by='room_type', figsize=(20,6), rot=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which neighborhoods have higher prices?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e1a65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.boxplot(column='price', by='neighborhood', figsize=(25,6), rot=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What about longitue and latitude?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e770860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.plot.scatter(x='longitude', y='latitude', c='price', figsize=(10,10), cmap='cool', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When prices are < $200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e7436d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt[frankfurt['price']<200].plot.scatter(x='longitude', y='latitude', c='price', figsize=(10,10), cmap='cool', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### And reviews?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286dee1a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.plot.scatter(x='reviews', y='price', figsize=(20,8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ead6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.scatter(np.log(1+frankfurt['reviews']), frankfurt['price'])\n",
    "plt.title('Price vs log(reviews)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Satisfaction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e298860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.plot.scatter(x='overall_satisfaction', y='price', figsize=(20,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df29780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.plot.scatter(x='bedrooms', y='price', figsize=(20,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accommodates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fZH+S1FqPl1Ie7jz/wWf4ZwMsKZOHjmas1cqxPHGb+7FWK5OHjtqW1QeO92Dt2f9Q17ptcAAAw3FGoVKt9d6m37iU8uokX6m13lFK+e7Z8nzf/gw+d+r3fWOSNybJJZdc0rQtgJG1Ye3KzLTbc2oz7XY2rF05pI5Gm+M9WFs2rmlUBwCg/850+9szsS3J9s52uT/MyW1vv55kTSllNszakOT+zseTSTYmSefzFyT56unftNb6rlrr1lrr1gsvvLCP7QMsLhOrxnP9js1ZMdbK6vHlWTHWyvU7Nls10yeO92Btumh1rrly7g+TrrnyEquUAACGqNT6pMVAC/+HnFyp9JbO3d/en2TXKYO699Za/1sp5aeSvKTW+m87g7r/ea31dU/1fbdu3Vp3797d9/6BZ86dsQbPMR8sx3uw3P0NAKD/Sil31Fq3Pt3zznSm0kLameQPSym/lOTTSd7Tqb8nye+VUvbl5AqlHx5Cb8ACcmes4ZhYNS7cGCDHe7A2XbRamAQAcJYYyEqlfrFSCc5eU0ems+26W3Js5omZMyvGWvnEzpe7AO8zK2cAAIBenM0rlYAlwJ2xhsPqMAAAYFD6OagbWMLcGWvwpo5MZ+euvTk2087h6eM5NtPOtbv2ZurI9LBbAwAARpBQCegLd8YavNnVYaeaXR0GAACw0Gx/A/pm+5b12bZpnfk+A2J1GAAAMEhWKgF9NbFqPFdsXCNQGgCrwwAAgEGyUglghFgdBgAADIpQCWDETKwaFyYBAAB9Z/sbS87Ukencuf8hd8QCAACAHlipxJJy854D2blrb8Zarcy027l+x+Zs37J+2G0BAADAomOlEkvG1JHp7Ny1N8dm2jk8fTzHZtq5dtdeK5YAAADgGRAqsWRMHjqasdbcU36s1crkoaND6mhpsN1w8BzzwXK8B2vfwcO5aff+7Dt4eNitANCQfzNh9Nj+xpKxYe3KzLTbc2oz7XY2rF05pI5Gn+2Gg+eYD9bNew7k2pv2Zlmr5ES75u2vdbz76a0fvCs33Hbf44+vufKSvO3qlwyxIwDOlPcoMJqsVGLJmFg1ntd9y4Y5tddt3eAuWX1iu+HgOeaDNXVkOm95/52ZPt7Oo4+dyPTxdn7+/Xc63n2y7+DhOYFSktxw631WLAEsAt6jwOgSKrFkTB2Zzo13TM6p3bh70j9mfWK74eA55oN1z/0PZ+ZEnVObOVFzz/0PD6mj0bZn/0ON6gCcPbxHgdElVGLJ8I/ZYNluOHiO+aCVhnV6sWXjmkZ1AM4e3qPA6BIqsWT4x2ywJlaN5/odm7NirJXV48uzYqyV63dstt2wjxzzwbr84vOz/LR/RZe3TtZZeJsuWp1rrrxkTu2aKy/JpotWD6kjAM6U9ygsBUt1EH2ptT79s85SW7durbt37x52GywiH9pzINcaEDhQU0emM3noaDasXemNw4A45oPzoT0H8gs33ZllpZUTtZ23v/YKryl9tu/g4ezZ/1C2bFwjUAJYZLxHYVSN4iD6UsodtdatT/s8oRJLjX/MgIXkNQUAYOmaOjKdbdfdkmMzT+yKWTHWyid2vnxRvzc801Bp+SCagbPJxKrxRf1/buDs4jUFAGDpmp3deyxPhEqzs3uXwntEM5VYcpbqXtdhcbwZdc5xAICla6nP7rVSiSVlFPe6ns0cb0adcxwAYGmbHUR/+uzepbBKKTFTiSVkVPe6nq0cb0adcxwAgFmjNmfzTGcq2f7GkjG71/VUs3tdWXiON6POOQ4AwKyJVeO5YuOakQiUmhAqsWQs9b2ug+Z4M+qc48NhhhUAwNlDqMSSMbvXdcVYK6vHl2fFWGtJ7XUdNMebUeccH7yb9xzItutuyevffXu2XXdLPrTnwLBbAgBY0sxUYskZtb2uZzvHm1HnHB8MM6wAAAbnTGcqufsbS87EqnEXIAPkeDPqnOODMTvD6lieCJVmZ1g5/gAAw2H7GwBw1jPDCgDg7CNUAgDOemZYDYfB6ADAU7H9DQBYFLZvWZ9tm9aZYTUgN+85kJ279mas1cpMu53rd2zO9i3rh90WAHAWsVLpLOCngIPleDPqnOOD5XgziqaOTGfnrr05NtPO4enjOTbTzrW79jrPAYA5rFQaMj8FHKyb9xzItTftzbJWyYl2zdtf63gzWk6e43dmWWnlRG3n7a+9wjneR17DB8tr+OAYjA4AnAkrlYbITwEHa+rIdN7y/jszfbydRx87kenj7fz8++90vBkZU0em8/M37sn08ZpHZ05k+njNm2/c4xzvE6/hg+U1fLAMRgcAzoRQaYhmfwp4qtmfArLw7rn/4cycqHNqMydq7rn/4SF1BAvrnvu/luNzrwFzvH2yzsLzGj5YXsMHy2B0AOBM2P42RH4KOGilYR0Wm9qwTi+8hg+a1/BBMxgdAHg6VioNkZ8CDtblF5+f5aed8ctbJ+swCi6/+IKMLZt7gT22rOTyiy8YUkejzWv4YF1+8flpnZYftYrX8H6bWDWeKzaucV4DAPOyUmnI/BRwcCZWjefXXrclv3DaEGPHnFExsWo8P/JtG3PDrfc9XvuRb9voHO8jr+GDVetTPwYAYLCESmeBiVXjLkQGxAUgo2zqyHRu3D05p3bj7sn87FXf4FzvI6/hg3HP/V970kbO2qm/7BsuHEZLAABLnu1vACPC4GhGm5lhAABnGyuVWFJu3nMgO3ftzVirlZl2O9fv2JztW9YPuy1YEAZHM8pmZ4adegc4M8MAAIbLSiWWjKkj09m5a2+OzbRzePp4js20c+2uvZk6Mj3s1mBBzA6OHl9ecu7YsowvLwZHMzImVo3nP//gFTlnWcn48lbOWVbyn3/QXDwAgGESKrFk2BrEUnByDUfp3GXdrdYZLTVJKSXLWiWlOL8BAIZNqMSSYWsQo252Nd708XYefexEpo9bjcfocH4DAJx9hEosGU9sDWo9/svWIEZJt1V3VuMxCqw2BQA4+wiVWFJ2f/GrmT7efvzX7nu/OuyWYMGcd86yHJuZuxrv2Ew7552zbEgdwcKx2hQA4OwjVGLJ2HfwcG647b45tRtuvS/7Dh4eUkdLw9SR6dy5/yFbVAbgkcdOZHzZ3Dkz48tKHnnsxJA6goUzu9p0xVgrq8eXZ8WY1aYAAMO2fNgNwKDs2f9Q1/qmi1YPuJul4eY9B7Jz196MtVqZabdz/Y7N2b5l/bDbGlkb1q5MaZXklFuul1axkoORsX3L+mzbtC6Th45mw9qVAiUAgCGzUoklY8vGNY3q9GZ2qO6xmXYOTx/PsRlDdfttYtV4XvctG+bUXrd1gwvvPrMab7AmVo3nio1rnNcAAGcBoRJLxqaLVueaKy+ZU7vmykusUuoTQ3UHb+rIdG68Y3JO7cbdk8KOPrp5z4Fsu+6WvP7dt2fbdbfkQ3sODLslAAAYGNvfWFLedvVLcs1LL82e/Q9ly8Y1AqU+MlR38GaDvGN54rjPBnlWdSy8U1fjzR7za3ftzbZN6xxvAACWBCuVWHLunXokn/ziV3Pv1CPDbmWkzQ7VHV9ecu7YsowvL4bq9pkgb7CsxgMAYKmzUokl5Xve8Vf5h4Mnw6Q/2j2ZF1x0Xj78pu8eblMjrCapteR4bScpT/d0ejSxajxbn7c2f7Nv6vHatz5vrSCvT4R4AAAsdVYqsWR89DNffjxQmvX3Bx/JRz/z5SF1NNqmjkznzTfemcdOtPPYiZrHTrTzphvvNN+nj/YdPDwnUEqSv943lX0HDw+po9HmFvcAACx1ViqxZHzkMwe71q960XMG3M3ou/Ufp3KiXefUTrRrbv3Hqbz6iouH1NVo++NPT3at/8KrvnHA3SwNbnEPAMBSZqUSS8b3vOiiRnV6021mlVlW/fPw0eON6iwMt7gHWLymjkznzv0PWUkN8AxZqcSScdWLnpPnnH9Ovvy1xx6vPff8c6xS6pPnTZzbqE7vXrPl4rzv9vvmrQMAc92850B27tqbsVYrM+12rt+xOdu3rB92WwCLipVKLBlTR6Yz9cjMnNqDj8z4yVSfXPn169I6bTZ3q5ys0x9bnz+R79o0Maf2XZsmsvX5E12+AgCWpqkj09m5a2+OzbRzePp4js20c+2uvd4XAjRkpRJLxj33P5yZE3Nn/MycqLnn/ofzsm949pC6Gl0Tq8bz6z+0JW95/50ppaTWml/9wStsEeqz3/tXL83uL0zl4597MC+7bJ1ACQDmMXnoaMZarRzLE3fxHGu1MnnoqPcqAA0IlVhCut3S3q3u+8UQ4+HY+nyrkwDgqWxYuzIz7fac2ky7nQ1rVw6pI4DFyfY3lozLLz5/3u1Yl198/nAagj7Zd/Bwbtq9P/sOHh52KwBwVppYNZ7rd2zOirFWVo8vz4qxVq7fsdkPwAAa6ttKpVLKxiQ3JHlOknaSd9Vaf6OU8qwkf5Tk0iRfTPK6WuuhUkpJ8htJvj/Jo0n+Za31U/3qj6XptDvcP+kxC8sAzMF76wfvyg23PTGs+5orL8nbrn7JEDsCgLOTFdUAvevnSqXjSX6+1vqNSV6a5KdKKS9K8otJPlprvSzJRzuPk+T7klzW+fXGJL/Vx95Ygn7v1i82qtMbAzAHb9/Bw3MCpSS54db7rFgCgC4mVo3nio1rBEoAz1DfQqVa65dmVxrVWg8n+WyS9UmuTvLeztPem+Q1nY+vTnJDPem2JGtKKc/tV38sPZ/ef6hRnd7MDsA81ewATPrjb/Y92KgOAADQi4HMVCqlXJrkm5LcnuSiWuuXkpPBU5LZ226tT7L/lC+b7NRgQfyzLtuuutXpjQGYgze+fP6X9G51gKczdWQ6d+5/yCpTAGBefb/SKKWsSrIryc/VWr/2VE+dp/akiTellDeWUnaXUnY/8MADC9UmS8BrvnljLlixbE7tghXL8ppv3jikjkabAZiDt75LYNetDvBUbt5zINuuuyWvf/ft2XbdLfnQngPDbgkAOMv0bVB3kpRSxnIyUPr9WusHOuWDpZTn1lq/1Nne9pVOfTLJqVf3G5Lcf/r3rLW+K8m7kmTr1q3GLHPGpo5M59GZuStnHp1pZ+rItKCjTwzAHKyZ4+1GdYBuTp2LdywnX0Ou3bU32zat81oOADyubyuVOndze0+Sz9Zaf+2UT30oyRs6H78hyc2n1K8pJ700ycOz2+RgIdxz/8OZOTE3h5w5UXPP/Q8PqaOlwQDMwblzcv5zuVsdoBtz8QCAM9HP7W/bkvxYkpeXUvZ0fn1/kl9J8spSyueSvLLzOEn+LMnnk+xL8ttJ/l0fe2NJmm+H5VPVYXF52WXrGtUBujEXDwA4E33b/lZr/Zt0v1q/ap7n1yQ/1a9+4PKLz29Uh8Vm6/Mn0kpy6mVgq1MHaGJ2Lt61u/ZmrNXKTLttLh4A8CR9nakEZ5MvPHCka92bZEbB+/72Czl9elK7U3/9dzx/GC0Bi5i5eADA03Gf6bPARz/z5ey86c589DNfHnYrI+3jn3uwUZ2F8b6//UJ+8J1/m/f97ReG3crIu3nv/GPoutVhMXKL+8E69Mhj+dzBwzn0yGPDbgUYAV7DYfRYqTRk3/OOv8o/HHwkSfJHuyfzgovOy4ff9N3DbWpEveyydfnNW/bNW6c/rvi//zwPHzuRJPnkFw/l7R/5+9z5f79qyF2Nrqs3Pzef/OKheeswCm7ecyDX3rQ3JUlN8vbXbs72LeuH3dbIeusH78oNt933+ONrrrwkb7v6JUPsaPRNHZm2MoyRdfOeA9l52pZar+Gw+FmpNEQf/cyXHw+UZv39wUesWOqTP/zf9zaq05v3/e0XHg+UZj187IQVS3300q+fPyDtVofFZOrIdN58452ZPt7OsePtTB9v50033umn3X2y7+DhOYFSktxw633Zd/DwkDoafTfvOZBt192S17/79my77pZ8aM+BYbcEC2bqyHR27tqbYzPtHJ4+nmMz7Vy7a6/XcBgBQqUh+shnDjaq05uPfOYrjer05qZPz/9muFud3u3Z/1CjOiwmt/7jVE6065zaiXbNrf84NaSORpvXk8Fywc2omzx0NGOtuZeeY61WJg8dHVJHwEIRKg3R97zookZ1evPCi1Y3qtObKy6+oFGd3i3v8orerQ6LyYNdLq671enNlo1rGtXpjQtuRt1288SGAAAZTUlEQVSGtSsz0557O5GZdjsb1q4cUkfAQnGpMURbLlnbqE5vvv+KixvV6c1Vl88fjnar07u7759/W0q3Oiwm37lp/m2c3er0ZtNFq3PNlZfMqV1z5SXZ5AcxfeGCm1E3sWo81+/YnBVjraweX54VY61cv2Oz2WEwAgzqHqLJQ0ezenx5Dk8ff7y2enx5Jg8d9QLbB3dPPnmA8RN1t1tfaJdffEFaJTl1t0qrnKzTH+evWNaoDovJbMhxw61zB0cLOfrnbVe/JNe89NLs2f9Qtmxc41j30ewF97WnDTH2fpBRsn3L+mzbtM4wehgxQqUh8lOpwfrE57/aqE5vDj3yWE4bf5J2PVn3JqI/po+faFSHxUbIMXibLlrtOA+IC26WgolV485tGDG2vw2RZaCDddULLmxUpzd/s+/BRnV699VHZhrVYTHadNHqvHbrRkEHI2li1Xiu2LjGe0EAFg0rlYZs+5b1ufiCFfn45x7Myy5bl63Pnxh2SyPreetWNarTm/Eu06G71endslZpVIfFaOrItJUcAABnCaHSkL31g3flhttOzof4zVv25ZorL8nbrn7JkLsaTffc/3CjOr1ZvWKsUZ3evWbL+rzv9v3z1umffQcP2441IDfvOZBrb9qbZa2SE+2at792c7Y7vwEAhsaSgSHad/Dw44HSrBtuvS/7DrpTUz9ceN45jer05vbPTzWq07s7vjj/fLBudXr31g/elVe84+N5y01784p3fDxvvfmuYbc0sqaOTOct778z08fbefSxE5k+3s7Pv//OTB2ZHnZrAABLllBpiD58z5cb1enN/753/gvrbnV6c/eX5l8B1q1O737vf9/XqE5v/GBgsO65/+HMnJg7/X/mRLXaFABgiIRKQ1Ub1unFiuXz7/bsVqc3O7psSelWp3erz5n/XO5Wpzd79j/UqE6vus0GMzMMAGBYhEpD9L2XP7dRnd688WVf16hOb75v88WN6vTue1/8nEZ1erNl45pGdXpz+cXn5/Q5/8tbJ+sAAAyHUGmIHnr0sUZ1evNLf3pPozq9+fW/+IdGdXp36JH5Xzu61enN2vPOedKd9Za1Staa09YXE6vG86Pffsmc2o9++yXuAAcAMERCpSH6+OcebFSnN1/46rFGdXpz+xe7DOruUqd3ew/MP1umW53eTB46mnPHls2pnTu2LJOHjg6po9E2dWQ6N+6enFO7cfekQd0AAEMkVBqil122rlGd3jz7vPnnynSr0xszlQZvxzd1OeZd6vRmw9qVmWm359Rm2u1sWLtySB2NtslDR1Pbc2cO1nYV4jFSpo5M5879DwlLAVg0hEpDtObc+bdIdKvTm+998fyzfLrV6c1VL5p/jk+3Or174XPnny3TrU5vJlaN5/odm7NirJXV48uzYqyV63dsth2rT847Z1mmT7v72/SJmvPOWdblK2BxuXnPgWy77pa8/t23Z9t1t+RDew4MuyUAeFqWaAzRH396smv9F171jQPuZvR97B++0qhObz58z5e61jddtHrA3SwNT7WlduvzJwbczdKwfcv6bNu0LpOHjmbD2pUCpT565LETWTHWyrGZJ1aHrRhr5ZHHTgyxK1gYU0ems3PX3hybaedYTp7j1+7am22b1nldAeCsJlQaooePHm9UpzfHT9s28XR1evPgkfmHQ3er07uvW3duozoLY2LVuIu+Aei2rdB2Q0bB5KGjGWu1Hg+UkmSs1crkoaNeXwA4q9n+NkSv2TL/tqtudXoz0eWOTN3q9GbdqvmPa7c6vTveblZnYew7eDg37d6ffQcPD7uVkTa73XB8eSvnnrMs48ttNxwEM34Gw4w2ABYrK5WGqNt2FNtU+uNbL31W7rr/yRd933rps4bQzej73sufm7d/5HPz1umPLRvXNKrTu7d+8K7ccNt9jz++5spL8rarXzLEjkZbnf3fWh5/RP/cvOdAdu7am7FWKzPtdq7fsTnb3WyhL2ZD02tPO95CUwDOdlYqDdFb/uhTjer05nf+9t5GdXrzu5/4fKM6vfuNv/i7RnV6s+/g4TmBUpLccOt9Viz1yezMmenjNY/OnMj08Zprd+21gqZPTp3xc3j6eI7NtB3vPtu+ZX0+sfPled+/+vZ8YufLBXgALApCpSH68GfmHxDdrQ6Lyf+4+8uN6vTuln+Yf1B3tzq92bP/oUZ1ejN56GijOr2ZnfFzqtkZP/TPxKrxXLFxjRVKACwaQqUhetFzVjWqw2Kyrsusqm51enfZhfMP5O5Wpzdrzx1rVKc3552zbM6d35Lk2Ew7552zbEgdjTYzfgCAMyFUGqKL185/odetTm+6nez+T9AfDz0606hO71aOzx/YdavTm7/78vzb3LrV6Y3jPVizM35WjLWyenx5VowZjA4APJlB3UP0mfsfblSnN91ugOXGWP2xrDV/XNetTu/adf6zuVsdFpMHjxxrVKd327esz7ZN6zJ56Gg2rF0pUAIAnsTV3RCNL58/0+tWh8Xkey+/qFGd3t1/aP6L6251evPtz5//zpHd6vTmxRdf0KjOwjDjBwB4KkKlIbp8/fmN6rCYfGuXC+tudXp37Pj8K5K61enNozMnGtXpzdjyZRlbVubWlpWMLTdTidExdWQ6d+5/yF32AFg0hEpDtG3ThY3q9ObFXQagd6vDYvOSDfMH0t3q9Ko0rNOLDWtXZllr7rFd1ioGRzMybt5zINuuuyWvf/ft2XbdLfnQngPDbgkAnpZQaYg+fNf8bxa61enN3V8+0qhOb274xBcb1endvoPzn8vd6vTm4gtWNKrTm4lV43ndt2yYU3vd1g22ZTESpo5MZ+euvTk2087h6eM5NtPOtbv2WrEEwFlPqDREH/7MVxrVYTG5476HGtXp3X1dZid1q9Ob+x/uMsOqS53eTB2Zzo13TM6p3bh70kU3I2Hy0NGMnXYji7FWK5OHjg6pIwA4M0KlIeo2j9ucbkbBeJfzuFud3nV7QfdC3y+1YZ1euOhmlG1YuzIz7bnz72babds7GTnmhsHoca0xRP9629c1qtObsYZ1evOvv6vL+d2lTu9+9Ns2NKrTm8svvmDeGT+XuxtZX7joZpRNrBrP9Ts2Z8VYK6vHl2fFWCvX79hseycjxdwwGE3WDAzRnfvn3wbUrU5vZhrW6c3UI481qtO7Fz5n/oHc3er0rpy2Kun0xyyc2Yvua3ftzVirlZl220U3I2X7lvXZtmldJg8dzYa1K53bjJRT54Ydy8kfEFy7a2+2bVrnXIdFzkqlIfrYvq82qsNi8oFPz//Tp251everH/n7RnV6M3noaFaOzf3ZzMqx5bZj9dH2Levzvp/4tvz4tkvzvp/4tmzfsn7YLcGCmlg1nis2rnGRzcixhRlGl5VKQ7RyefLo8fnrsNitHFuWR2eefIKvHFs2hG6WhiPTJxrV6c2GtStz9LRz/OjMcdux+uitH7wrN9x2X5LkN2/Zl2uuvCRvu/olQ+4KFs7UkWkrlRhJtjDD6LJSaYi+4aJVjeqwmDy7y5vhbnV6t6ZLIt2tTu9KKU/5mIWz7+DhxwOlWTfcel/2HTw8pI5gYZk3wygzNwxGlyuNIfr7g480qsNiMtnlturd6vTu0Zn5VyR1q9ObyUNHs6xVMnPiiTlKy1olk4eOepPcB3+z78Gu9U0XrR5wN7CwzJthKTA3DEaTUGmITpyYf6BrtzosJu06f5DRrU7vzh9fnkdnnjx6/vxxL/X9cN45y3JsZu5S/mMz7Zx3ji2e/bCuy8VHtzosJrPzZmYDpeSJeTMuvBklE6vGndMwYmx/G6LxsfkPf7c6LCaPdbmtXrc6vZtYvaJRnd7c//D8w0W71enNlV8/kWWtudsLl7VKrvz6iSF1BAvHjDYAFivpxRAdfqzdqA6LyeoV87+8dKvTu1Ur5l8h061Ob752dJ47LTxFnd5MrBrPO153RcaXtx7/9Y7XXeEn3owMM9oAWIzsiQD64uGj84ej3er07six+cOMbnV6c36XAejd6vTOPA5G1eSho1mxfFlmTjzxer1i+TLb3wA463nnC/RFt8lJJir1z7ldZvl0q9Obiy+Yf1tKtzoLwzwORpHbrQOwWNmHAvTFeefMv2y/W53ePW9iVaM6vfm7L89/K/tudYBuZm+3fur2TrdbB2AxECoBfbFl/dpGdXp32YXnNarTmwePHGtUB3gqu7/41Uwfbz/+a/e9Xx12SwDwtIRKQF984auPNKrTu09PPtyoTm++c9OFjeoA3ew7eDg33HbfnNoNt96XfQetfOy3qSPTuXP/Q5k6Mj3sVpaEX/6Tu3Pl//e/8st/cvewWwEWiJlKQF90e3PmTVv/rD13rFGd3my6aHW+a9NE/nrf1OO179o0kU0XrR5iV8Bi9Df7Huxa95rSPzfvOZCdu/ZmrNXKTLud63dszvYt64fd1sj6ul/8H5mdHPbbn7g37/nEvfn8r/zAUHsCemelEtAXy7q8unSr07vVK+YPj7rV6c3Ukel88t5Dc2qfvPeQ4BRobN2qcxrV6d3Ukens3LU3x2baOTx9PMdm2rl2116v4X3yy39yd06//2+7UwcWN5d3QF/U0985PE2d3t331Ucb1enN5KGjGWvN/Wd0rNXK5KGjQ+poabBVhVH0wuec36hO77yGD9af3v3lRnVg8bD9DeiLZctbyYknJ0jLlsuy++WSZ53bqE5v3AJ88GxVYVQ98tiJjC8rmT5RH6+NLyt55LETQ+xqtHkNH6xXv/g5+e1P3DtvHVjcXN0BfbF145pGdXp3+NhMozq9mb0F+IqxVlaPL8+KMbcA7ydbVRhlG9auTGmVObXSKgKOPvIaPlj/8Z+++EkXnq1OHVjcrFQC+mL52PwvL93q9O7g1+a/lX23Or3bvmV9tm1al8lDR7Nh7UoXI300u1Xl2ClTOWa3qjjuLHazAce1p63Ec273l9fwwfr8r/xAfvlP7s6f3v3lvPrFzxEowYhwdQf0xbdesib/67NfmbdOf1yxYU1u+fsn30Hoig2OeT9NrBp3ITIAtqow6gQcw+E1fLD+4z99sTAJRoztb0BfTKxe0ahO777pec9qVIfFxFYVloKJVeO5YuMa5zUAi4aVSiwZK5Ylx+aZd7li2eB7WQq2dJmd1K1O7y6/+PwsbyXHT1nMsbx1sg6jwEoOAICzi5VKQ9Qty5Bx9MfLv/HZjer0ZtNFq3PNlZfMqV1z5SXZdNHqIXU0+iZWjefXXrcl48tLzh1blvHlJb/2ui0uvBkpVnIAAJw9rFQaoosuGM/9Dz/5rjUXXeCNcj+8+ZUvzJ/d/eQZP29+5QuH0M0SUZ/+KSwsKzkAAIBBsVJpiLZfcXGjOr2xcmaw9h08nBtuu29O7YZb78u+g4eH1NHSYSUHAAAwCGfVSqVSyquS/EZO7gB7d631V4bcUl99x6YL886Pf2HeOv3xtqtfkmteemn27H8oWzauESj10Z79D3WtO+4AAACL31kTKpVSliX5r0lemWQyySdLKR+qtX5muJ31j6G6w7HpotVCjQEwqBsAAGC0nU3b374tyb5a6+drrY8l+cMkVw+5p74yVJdRZrshAADAaDtrViolWZ9k/ymPJ5N8++lPKqW8Mckbk+SSSy45/dOLjqG6jDLbDQEAAEbX2RQqlXlqT7p3VK31XUnelSRbt24diXtLTawaFyYxsmw3BAAAGE1n0/a3ySQbT3m8Icn9Q+oFAAAAgKdwNoVKn0xyWSnl+aWUc5L8cJIPDbknAAAAAOZx1mx/q7UeL6X8dJIPJ1mW5HdqrfcMuS0AAAAA5nHWhEpJUmv9syR/Nuw+AAAAAHhqZ9P2NwAAAAAWCaESAAAAAI0JlQAAAABoTKgEAAAAQGNCJQAAAAAaEyoBAAAA0FiptQ67h2eslPJAknuH3QeL0rokDw67Cegj5zijzPnNKHN+M+qc44yyUTq/n1drvfDpnrSoQyV4pkopu2utW4fdB/SLc5xR5vxmlDm/GXXOcUbZUjy/bX8DAAAAoDGhEgAAAACNCZVYqt417Aagz5zjjDLnN6PM+c2oc44zypbc+W2mEgAAAACNWakEAAAAQGNCJZaUUsrGUspfllI+W0q5p5Tys8PuCRZaKWVZKeXTpZQ/HXYvsNBKKWtKKTeVUv6u81p+5bB7goVSSnlT5/3J3aWUPyilrBh2T9CLUsrvlFK+Ukq5+5Tas0opf1FK+Vzn97XD7BGeqS7n99s771H2llL+uJSyZpg9DoJQiaXmeJKfr7V+Y5KXJvmpUsqLhtwTLLSfTfLZYTcBffIbSf681vrCJFfEuc6IKKWsT/Lvk2yttb44ybIkPzzcrqBnv5vkVafVfjHJR2utlyX5aOcxLEa/myef33+R5MW11s1J/iHJfxh0U4MmVGJJqbV+qdb6qc7Hh3PyYmT9cLuChVNK2ZDkB5K8e9i9wEIrpZyf5GVJ3pMktdbHaq0PDbcrWFDLk6wspSxPcm6S+4fcD/Sk1vrxJF89rXx1kvd2Pn5vktcMtClYIPOd37XWj9Raj3ce3pZkw8AbGzChEktWKeXSJN+U5PbhdgIL6teTXJukPexGoA++LskDSf57Z4vnu0sp5w27KVgItdYDSX41yX1JvpTk4VrrR4bbFfTFRbXWLyUnf+Cb5NlD7gf65SeS/M9hN9FvQiWWpFLKqiS7kvxcrfVrw+4HFkIp5dVJvlJrvWPYvUCfLE/yzUl+q9b6TUkeiW0TjIjOXJmrkzw/ycVJziulvH64XQHwTJRS/mNOjl75/WH30m9CJZacUspYTgZKv19r/cCw+4EFtC3J9lLKF5P8YZKXl1LeN9yWYEFNJpmstc6uML0pJ0MmGAWvSPKFWusDtdaZJB9I8h1D7gn64WAp5blJ0vn9K0PuBxZUKeUNSV6d5F/UWuuw++k3oRJLSiml5OQsjs/WWn9t2P3AQqq1/oda64Za66U5Odz1llqrn3IzMmqtX06yv5Tygk7pqiSfGWJLsJDuS/LSUsq5nfcrV8UgekbTh5K8ofPxG5LcPMReYEGVUl6VZGeS7bXWR4fdzyAIlVhqtiX5sZxcwbGn8+v7h90UAGfsZ5L8fillb5ItSf7fIfcDC6KzAu+mJJ9KcldOvk9/11Cbgh6VUv4gya1JXlBKmSyl/GSSX0nyylLK55K8svMYFp0u5/d/SbI6yV90rjXfOdQmB6AsgdVYAAAAACwwK5UAAAAAaEyoBAAAAEBjQiUAAAAAGhMqAQAAANCYUAkAAACAxoRKAABnsVLKpaWUu8/gOT86qJ4AABKhEgDAKLg0iVAJABgooRIAMJJKKR8spdxRSrmnlPLGTu1VpZRPlVLuLKV8tFNbVUr576WUu0ope0spOzr1H+nU7i6lXHfK9z1SSrmu873/Vynl20opf1VK+XwpZXvnOf+y8+f/SSnlC6WUny6lvLmU8ulSym2llGd1nrel83hvKeWPSylrO/Vv6fR4a5KfOuXPvrSU8tedv8OnSinf0fnUryT5rlLKnlLKm0opy0opby+lfLLzvf9N5+ufW0r5eOd5d5dSvqvv/yEAgJFVaq3D7gEAYMGVUp5Va/1qKWVlkk8muSrJ7iQvq7V+4ZTPX5dkvNb6c52vW5tkZZLbknxLkkNJPpLkN2utHyyl1CTfX2v9n6WUP05yXpIfSPKiJO+ttW4ppfzLJP8pyTclWZFkX5KdtdZ3llLekeTeWuuvl1L2JvmZWuvHSilvS3J+rfXnTqu/Pcn31VpfXEo5N0m71nqslHJZkj+otW4tpXx3krfUWl/d+Tu8Mcmza62/VEoZT/KJJD+Y5J8nWVFr/eVSyrIk59ZaD/ftPwIAMNKWD7sBAIA++fellH/W+Xhjkjcm+Xit9QtJUmv9audzr0jyw7NfVGs9VEp5WZK/qrU+kCSllN9P8rIkH0zyWJI/7zz9riTTtdaZUspdObkNbdZfdgKbw6WUh5P8ySlfs7mUckGSNbXWj3Xq703y/nnqv5fk+zofjyX5L6WULUlOJPmGLn/37+n8Ga/tPL4gyWU5Ga79TillLMkHa617uh08AICnI1QCAEZOZ+XOK5JcWWt9tJTyV0nuTPKC+Z6e5PSl2+Upvv1MfWKpdzvJdJLUWtullFPfW02f8nH7lMftPPV7sPn6mfWmJAeTXJGTYwyOPcX3+Jla64ef9ImTgdkPJPm9Usrba603PEUvAABdmakEAIyiC5Ic6gRKL0zy0iTjSf5JKeX5ycntcZ3nfiTJT89+YWf72+2d567rbBP7kSQfywKqtT6c5NApc41+LMnHaq0PJXm4lPKdnfq/OO3v9aVaa7vz/GWd+uEkq0953oeT/B+dFUkppXxDKeW8Usrzknyl1vrbSd6T5JsX8u8EACwtVioBAKPoz5P8285sor/PyflID+TkFrgPlFJaSb6S5JVJfinJfy2l3J2TW8r+n1rrB0op/yHJX+bkqp8/q7Xe3Ic+35DknZ1ZSZ9P8uOd+o/n5Da1R3MyIJr135LsKqX8YKe3Rzr1vUmOl1LuTPK7SX4jJ7fifaqUUjp/99ck+e4kv1BKmUlyJMk1ffg7AQBLhEHdAAAAADRm+xsAAAAAjQmVAAAAAGhMqAQAAABAY0IlAAAAABoTKgEAAADQmFAJAAAAgMaESgAAAAA0JlQCAAAAoLH/H/UyCIWwu4cbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e0dd978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frankfurt.plot.scatter(x='accommodates', y='price', figsize=(20,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add some additional features based on our understanding to make the life easier for the Linear Regression algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "frankfurt['logreviews'] = np.log(1 + frankfurt['reviews'])\n",
    "frankfurt['bedrooms_per_accommodates'] = frankfurt['bedrooms'] / frankfurt['accommodates']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_type</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>logreviews</th>\n",
       "      <th>bedrooms_per_accommodates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Bahnhofsviertel</td>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.108833</td>\n",
       "      <td>8.668395</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Westend-Süd</td>\n",
       "      <td>27</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.120212</td>\n",
       "      <td>8.657187</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Rödelheim</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>50.128278</td>\n",
       "      <td>8.596030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Oberrad</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>50.099801</td>\n",
       "      <td>8.722627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shared room</td>\n",
       "      <td>Innenstadt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>50.115677</td>\n",
       "      <td>8.694514</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     room_type     neighborhood  reviews  overall_satisfaction  accommodates  \\\n",
       "0  Shared room  Bahnhofsviertel       51                   4.5            10   \n",
       "1  Shared room      Westend-Süd       27                   4.5             2   \n",
       "2  Shared room        Rödelheim        0                   0.0             2   \n",
       "3  Shared room          Oberrad        0                   0.0             6   \n",
       "4  Shared room       Innenstadt        0                   0.0             2   \n",
       "\n",
       "   bedrooms  price   latitude  longitude  logreviews  \\\n",
       "0       1.0   80.0  50.108833   8.668395    3.951244   \n",
       "1       1.0   52.0  50.120212   8.657187    3.332205   \n",
       "2       1.0   35.0  50.128278   8.596030    0.000000   \n",
       "3       1.0  346.0  50.099801   8.722627    0.000000   \n",
       "4       1.0  149.0  50.115677   8.694514    0.000000   \n",
       "\n",
       "   bedrooms_per_accommodates  \n",
       "0                   0.100000  \n",
       "1                   0.500000  \n",
       "2                   0.500000  \n",
       "3                   0.166667  \n",
       "4                   0.500000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frankfurt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shall we remove properties without any reviews?\n",
    "I would go for it ...\n",
    "\n",
    "During this project we found out that properties with very few number of reviews are very difficult to predict. And we opted to fix the minimum number of reviews to 10. You may change this number and experiment how the results change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amount of data original: 1995\n",
      "amount of data after removing \"few reviews\" properties: 493\n"
     ]
    }
   ],
   "source": [
    "data = frankfurt.copy()\n",
    "print('amount of data original: ' + str(len(data)))\n",
    "data = data[data['reviews'] > 10] ### <<---  Change 10 for any other number to change the minimum number of reviews you require\n",
    "print('amount of data after removing \"few reviews\" properties: ' + str(len(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's encode the room_type and neighborhood using one-hot encoding. E.g. for split the neighborhood variable in as many different neighborhoods are, and set all to 0 except for the right neighborhood, which will be set to 1. More info in: https://en.wikipedia.org/wiki/One-hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>logreviews</th>\n",
       "      <th>bedrooms_per_accommodates</th>\n",
       "      <th>room_type_Entire home/apt</th>\n",
       "      <th>...</th>\n",
       "      <th>neighborhood_Sachsenhausen-N.</th>\n",
       "      <th>neighborhood_Sachsenhausen-S.</th>\n",
       "      <th>neighborhood_Schwanheim</th>\n",
       "      <th>neighborhood_Seckbach</th>\n",
       "      <th>neighborhood_Sindlingen</th>\n",
       "      <th>neighborhood_Sossenheim</th>\n",
       "      <th>neighborhood_Unterliederbach</th>\n",
       "      <th>neighborhood_Westend-Nord</th>\n",
       "      <th>neighborhood_Westend-Süd</th>\n",
       "      <th>neighborhood_Zeilsheim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.108833</td>\n",
       "      <td>8.668395</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.120212</td>\n",
       "      <td>8.657187</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>50.116308</td>\n",
       "      <td>8.679903</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>50.114359</td>\n",
       "      <td>8.685874</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.107130</td>\n",
       "      <td>8.626485</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    reviews  overall_satisfaction  accommodates  bedrooms  price   latitude  \\\n",
       "0        51                   4.5            10       1.0   80.0  50.108833   \n",
       "1        27                   4.5             2       1.0   52.0  50.120212   \n",
       "6        21                   4.5             2       1.0  109.0  50.116308   \n",
       "10       31                   4.5             3       1.0   79.0  50.114359   \n",
       "14       15                   5.0             2       1.0   60.0  50.107130   \n",
       "\n",
       "    longitude  logreviews  bedrooms_per_accommodates  \\\n",
       "0    8.668395    3.951244                   0.100000   \n",
       "1    8.657187    3.332205                   0.500000   \n",
       "6    8.679903    3.091042                   0.500000   \n",
       "10   8.685874    3.465736                   0.333333   \n",
       "14   8.626485    2.772589                   0.500000   \n",
       "\n",
       "    room_type_Entire home/apt           ...            \\\n",
       "0                           0           ...             \n",
       "1                           0           ...             \n",
       "6                           0           ...             \n",
       "10                          0           ...             \n",
       "14                          0           ...             \n",
       "\n",
       "    neighborhood_Sachsenhausen-N.  neighborhood_Sachsenhausen-S.  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "6                               0                              0   \n",
       "10                              0                              0   \n",
       "14                              0                              0   \n",
       "\n",
       "    neighborhood_Schwanheim  neighborhood_Seckbach  neighborhood_Sindlingen  \\\n",
       "0                         0                      0                        0   \n",
       "1                         0                      0                        0   \n",
       "6                         0                      0                        0   \n",
       "10                        0                      0                        0   \n",
       "14                        0                      0                        0   \n",
       "\n",
       "    neighborhood_Sossenheim  neighborhood_Unterliederbach  \\\n",
       "0                         0                             0   \n",
       "1                         0                             0   \n",
       "6                         0                             0   \n",
       "10                        0                             0   \n",
       "14                        0                             0   \n",
       "\n",
       "    neighborhood_Westend-Nord  neighborhood_Westend-Süd  \\\n",
       "0                           0                         0   \n",
       "1                           0                         1   \n",
       "6                           0                         0   \n",
       "10                          0                         0   \n",
       "14                          0                         0   \n",
       "\n",
       "    neighborhood_Zeilsheim  \n",
       "0                        0  \n",
       "1                        0  \n",
       "6                        0  \n",
       "10                       0  \n",
       "14                       0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.copy().drop('price', axis = 1)\n",
    "y = data['price'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data in training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data to have similar dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we just take the median value, our baseline, we would say that an overnight stay in Frankfurt costs: 57.0\n"
     ]
    }
   ],
   "source": [
    "baseline = y_train.median() #median train\n",
    "print('If we just take the median value, our baseline, we would say that an overnight stay in Frankfurt costs: ' + str(baseline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And we will be right +- 47.1899046065384\n"
     ]
    }
   ],
   "source": [
    "baseline_error = np.sqrt(mean_squared_error(y_pred=np.ones_like(y_test) * baseline, y_true=y_test))\n",
    "print('And we will be right +- ' + str(baseline_error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "alphas = [1000, 100, 50, 20, 10, 1, 0.1, 0.01]\n",
    "l1_ratios = [0.001, 0.01, 0.05, 0.1, 0.3, 0.5, 0.7, 0.9]\n",
    "ridge = RidgeCV(alphas=alphas)\n",
    "lasso = LassoCV(alphas=alphas, max_iter=10000)\n",
    "elastic = ElasticNetCV(alphas=alphas, l1_ratio=l1_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression mrse_train: 30.631052305737853, mrse_test: 677509406148890.5\n",
      "Ridge mrse_train: 31.32304098021783, mrse_test: 34.791803147134644 best alpha: 100.0\n",
      "Lasso mrse_train: 31.43232181185478, mrse_test: 34.16718989710831 best alpha: 1.0\n",
      "ElasticNet mrse_train: 31.56566254997469, mrse_test: 34.41199228947752 best alpha: 1.0 best l1: 0.9\n"
     ]
    }
   ],
   "source": [
    "for model, name in zip([lr, ridge, lasso, elastic], ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet']):\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    y_pred_train = model.predict(X_train_scaled)\n",
    "    mrse_train = np.sqrt(mean_squared_error(y_pred=y_pred_train, y_true=y_train))\n",
    "    y_pred = model.predict(X_test_scaled)\n",
    "    mrse_test = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
    "    best_alpha = ''\n",
    "    if name != 'LinearRegression':\n",
    "        best_alpha = ' best alpha: ' + str(model.alpha_)\n",
    "    best_l1 = ''\n",
    "    if name == 'ElasticNet':\n",
    "        best_l1 = ' best l1: '+ str(model.l1_ratio_)\n",
    "    print(name + ' mrse_train: ' + str(mrse_train) + ', mrse_test: ' + str(mrse_test) + best_alpha + best_l1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The testing error from Ridge, Lasso and ElasticNet are much better than the baseline testing errors !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Non-zero Lasso coefficients ordered by importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "room_type_Entire home/apt, 18.653967048926283\n",
      "bedrooms, 9.901891866645677\n",
      "neighborhood_Westend-Nord, 6.560741105279131\n",
      "overall_satisfaction, 5.319197012800603\n",
      "neighborhood_Gallus, 4.972585149672787\n",
      "accommodates, 4.235241149707108\n",
      "neighborhood_Gutleutviertel, 3.9328494977940287\n",
      "neighborhood_Fechenheim, 1.0643855768850217\n",
      "neighborhood_Westend-Süd, 1.0131529422612837\n",
      "neighborhood_Nied, 0.5527358488132461\n",
      "neighborhood_Bockenheim, 0.5405469785877816\n",
      "neighborhood_Sachsenhausen-N., 0.16467591306490312\n"
     ]
    }
   ],
   "source": [
    "order = np.argsort(np.abs(lasso.coef_))[::-1]\n",
    "for i in order:\n",
    "    coef_ = lasso.coef_[i]\n",
    "    if coef_ > 0:\n",
    "        print(X.columns[i] + ', ' + str(lasso.coef_[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2286e0ba0f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df8de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_train = lasso.predict(X_train_scaled)\n",
    "diff = y_train - y_pred_train\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.scatter(np.arange(len(diff)), diff, label = 'residuals')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "      <th>overall_satisfaction</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>logreviews</th>\n",
       "      <th>bedrooms_per_accommodates</th>\n",
       "      <th>room_type_Entire home/apt</th>\n",
       "      <th>room_type_Private room</th>\n",
       "      <th>...</th>\n",
       "      <th>neighborhood_Sachsenhausen-N.</th>\n",
       "      <th>neighborhood_Sachsenhausen-S.</th>\n",
       "      <th>neighborhood_Schwanheim</th>\n",
       "      <th>neighborhood_Seckbach</th>\n",
       "      <th>neighborhood_Sindlingen</th>\n",
       "      <th>neighborhood_Sossenheim</th>\n",
       "      <th>neighborhood_Unterliederbach</th>\n",
       "      <th>neighborhood_Westend-Nord</th>\n",
       "      <th>neighborhood_Westend-Süd</th>\n",
       "      <th>neighborhood_Zeilsheim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.888889</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>5.111111</td>\n",
       "      <td>2.222222</td>\n",
       "      <td>50.104607</td>\n",
       "      <td>8.655607</td>\n",
       "      <td>3.356490</td>\n",
       "      <td>0.446296</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.714997</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>3.218868</td>\n",
       "      <td>1.301708</td>\n",
       "      <td>0.011531</td>\n",
       "      <td>0.025057</td>\n",
       "      <td>0.617839</td>\n",
       "      <td>0.112971</td>\n",
       "      <td>0.440959</td>\n",
       "      <td>0.440959</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.440959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.089262</td>\n",
       "      <td>8.625551</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.101233</td>\n",
       "      <td>8.634469</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.102222</td>\n",
       "      <td>8.653955</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50.107755</td>\n",
       "      <td>8.669467</td>\n",
       "      <td>3.828641</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>50.129872</td>\n",
       "      <td>8.699099</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         reviews  overall_satisfaction  accommodates  bedrooms   latitude  \\\n",
       "count   9.000000              9.000000      9.000000  9.000000   9.000000   \n",
       "mean   32.888889              4.666667      5.111111  2.222222  50.104607   \n",
       "std    20.714997              0.353553      3.218868  1.301708   0.011531   \n",
       "min    11.000000              4.000000      2.000000  1.000000  50.089262   \n",
       "25%    19.000000              4.500000      3.000000  1.000000  50.101233   \n",
       "50%    23.000000              4.500000      4.000000  2.000000  50.102222   \n",
       "75%    45.000000              5.000000      6.000000  3.000000  50.107755   \n",
       "max    73.000000              5.000000     12.000000  4.000000  50.129872   \n",
       "\n",
       "       longitude  logreviews  bedrooms_per_accommodates  \\\n",
       "count   9.000000    9.000000                   9.000000   \n",
       "mean    8.655607    3.356490                   0.446296   \n",
       "std     0.025057    0.617839                   0.112971   \n",
       "min     8.625551    2.484907                   0.250000   \n",
       "25%     8.634469    2.995732                   0.333333   \n",
       "50%     8.653955    3.178054                   0.500000   \n",
       "75%     8.669467    3.828641                   0.500000   \n",
       "max     8.699099    4.304065                   0.600000   \n",
       "\n",
       "       room_type_Entire home/apt  room_type_Private room  \\\n",
       "count                   9.000000                9.000000   \n",
       "mean                    0.777778                0.222222   \n",
       "std                     0.440959                0.440959   \n",
       "min                     0.000000                0.000000   \n",
       "25%                     1.000000                0.000000   \n",
       "50%                     1.000000                0.000000   \n",
       "75%                     1.000000                0.000000   \n",
       "max                     1.000000                1.000000   \n",
       "\n",
       "                ...            neighborhood_Sachsenhausen-N.  \\\n",
       "count           ...                                      9.0   \n",
       "mean            ...                                      0.0   \n",
       "std             ...                                      0.0   \n",
       "min             ...                                      0.0   \n",
       "25%             ...                                      0.0   \n",
       "50%             ...                                      0.0   \n",
       "75%             ...                                      0.0   \n",
       "max             ...                                      0.0   \n",
       "\n",
       "       neighborhood_Sachsenhausen-S.  neighborhood_Schwanheim  \\\n",
       "count                       9.000000                      9.0   \n",
       "mean                        0.222222                      0.0   \n",
       "std                         0.440959                      0.0   \n",
       "min                         0.000000                      0.0   \n",
       "25%                         0.000000                      0.0   \n",
       "50%                         0.000000                      0.0   \n",
       "75%                         0.000000                      0.0   \n",
       "max                         1.000000                      0.0   \n",
       "\n",
       "       neighborhood_Seckbach  neighborhood_Sindlingen  \\\n",
       "count                    9.0                      9.0   \n",
       "mean                     0.0                      0.0   \n",
       "std                      0.0                      0.0   \n",
       "min                      0.0                      0.0   \n",
       "25%                      0.0                      0.0   \n",
       "50%                      0.0                      0.0   \n",
       "75%                      0.0                      0.0   \n",
       "max                      0.0                      0.0   \n",
       "\n",
       "       neighborhood_Sossenheim  neighborhood_Unterliederbach  \\\n",
       "count                      9.0                           9.0   \n",
       "mean                       0.0                           0.0   \n",
       "std                        0.0                           0.0   \n",
       "min                        0.0                           0.0   \n",
       "25%                        0.0                           0.0   \n",
       "50%                        0.0                           0.0   \n",
       "75%                        0.0                           0.0   \n",
       "max                        0.0                           0.0   \n",
       "\n",
       "       neighborhood_Westend-Nord  neighborhood_Westend-Süd  \\\n",
       "count                   9.000000                       9.0   \n",
       "mean                    0.111111                       0.0   \n",
       "std                     0.333333                       0.0   \n",
       "min                     0.000000                       0.0   \n",
       "25%                     0.000000                       0.0   \n",
       "50%                     0.000000                       0.0   \n",
       "75%                     0.000000                       0.0   \n",
       "max                     1.000000                       0.0   \n",
       "\n",
       "       neighborhood_Zeilsheim  \n",
       "count                     9.0  \n",
       "mean                      0.0  \n",
       "std                       0.0  \n",
       "min                       0.0  \n",
       "25%                       0.0  \n",
       "50%                       0.0  \n",
       "75%                       0.0  \n",
       "max                       0.0  \n",
       "\n",
       "[8 rows x 52 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[np.abs(diff) > 100].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size high error: 12\n",
      "size low error: 142\n"
     ]
    }
   ],
   "source": [
    "high_error = X_train[np.abs(diff) > 80]\n",
    "print('size high error: ' + str(len(high_error)))\n",
    "low_error = X_train[np.abs(diff) < 10]\n",
    "print('size low error: ' + str(len(low_error)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WnMjMMiBpgqQ70z1Nl0s6s9kxmbWKqlctmtnWsRk4OyLulbQTsEzSwoh4uNmBmTWbj8jMMhAR6yLi3jS+ieIG3eOaG5VZa/ARmVlmJE0CpgNLu5TPAeYATJw4cavHZVtIqvb6iMbEMQD4iMwsI5J2BG4EzoqIF8rzfP9SG6ycyMwyIWkYRRK7NiJuanY8Zq3CicwsA5JE8ey/FRHxjWbHY9ZKnMjM8nAIcAowU9L9aTiq2UGZtQJf7GGWgYi4G6h4dYDZwOQjMjMzy5oTmZmZZc2JzMzMsuZEZmZmWXMiMzOzrDmRmZlZ1pzIzMwsa05kZmaWNScyMzPLWuVEJmmIpPsk3ZKmJ0taKmmVpOslbVs9TDMzs9oacUR2JsVD/jpdCFwUEVOA54HZDdiGmZlZTZUSmaTxwNHAZWlawExgflpkHnB8lW2YmZl1p+oR2cXAOcBraXoXYENEbE7T7dR5HLukOZLaJLV1dHRUDMPMzAarPicySccA6yNiWbm4xqI1n8ftp9mamVkjVHmMyyHAsemZSNsDO1McoY2QNDQdlY0H1lYP08zMrLY+H5FFxNyIGB8Rk4ATgTsi4mTgTuCEtNgsYEHlKM3MzOroj9+RnQt8XtJqinNml/fDNszMzIAGPSE6IhYDi9P4o8ABjVivmZlZT3xnDzMzy5oTmZmZZc2JzMzMsuZEZmZmWXMiMzOzrDmRmZlZ1pzIzMwsa05kZmaWNScyMzPLmhOZmZllzYnMzMyy5kRmlgFJV0haL+mhZsdi1mqcyMzycBVwZLODMGtFTmRmGYiIu4Dnmh2HWStyIjMzs6w15HlkZtZ8kuYAcwAmTpzY5GgyIDU7grw14v2LqL4OfERmNmBExKURMSMiZowePbrZ4ZhtNU5kZmaWNScyswxIug74BbCXpHZJs5sdk1mr8DkyswxExEnNjsGsVfX5iEzSBEl3SlohabmkM1P5KEkLJa1Kf0c2LlwzM7M3q9K1uBk4OyKmAgcBn5a0D3AesCgipgCL0rSZmVm/6HMii4h1EXFvGt8ErADGAccB89Ji84DjqwZpZmZWT0Mu9pA0CZgOLAV2j4h1UCQ7YLdGbMPMzKyWyolM0o7AjcBZEfHCFrxujqQ2SW0dHR1VwzAzs0GqUiKTNIwiiV0bETel4mckjUnzxwDra73WP940M7NGqHLVooDLgRUR8Y3SrJuBWWl8FrCg7+GZmZl1r8rvyA4BTgF+Jen+VPYF4KvADekHm08CH60WopmZWX19TmQRcTdQ766Rh/d1vWZmZlvCt6gyM7OsOZGZmVnWnMjMzCxrTmRmZpa1QXH3+6oPMm3QQ0zNzKwf+IjMzMyy5kRmZmZZGxRdi1VV7ZoEd0+amfUXH5GZmVnWnMjMzCxrTmRmZpY1JzIzM8uaE5mZmWXNiczMzLLmRGZmZllzIjMzs6w5kZmZWdZ8Z49M+MbHZma1+YjMzMyy5iOyraQR92s0M7O38hGZmZllrV8SmaQjJa2UtFrSef2xDbPBxu3KrLaGJzJJQ4B/Bz4M7AOcJGmfRm/HbDBxuzKrrz/OkR0ArI6IRwEk/RA4Dni4H7ZlvdTsc3TNvmpyADxTzu3KrI7+6FocB6wpTbenMjPrO7crszr644is1nfft3yXlTQHmJMmX5S0EtgVeLYfYmolg7KOzT4ibIQadehazz36c/M1yt7Uruq0qWbpfj/Pb4dovXZb7T1sjfr0XIdetan+SGTtwITS9HhgbdeFIuJS4NJymaS2iJjRDzG1DNdx4NjK9eyxXdVqU80y0PYB16e19UfX4v8BUyRNlrQtcCJwcz9sx2wwcbsyq6PhR2QRsVnSZ4DbgSHAFRGxvNHbMRtM3K7M6uuXO3tExG3AbX14aUt0i/Qz13Hg2Kr1rNCummGg7QOuTwtTNPu6aDMzswp8iyozM8taSySygXTrHUlXSFov6aFS2ShJCyWtSn9HpnJJ+laq94OS9m9e5L0naYKkOyWtkLRc0pmpfMDUU9L2ku6R9ECq45dS+WRJS1Mdr08XXiBpuzS9Os2f1Mz4t5Yt3RdyIWmIpPsk3ZKma37uOZA0QtJ8SY+kz+ng3D+frpqeyAbgrXeuAo7sUnYesCgipgCL0jQUdZ6ShjnAJVspxqo2A2dHxFTgIODT6TMbSPV8BZgZEfsB04AjJR0EXAhclOr4PDA7LT8beD4i3glclJYbDLZ0X8jFmcCK0nS9zz0H3wR+HBF7A/tR1Cv3z+fNIqKpA3AwcHtpei4wt9lxVazTJOCh0vRKYEwaHwOsTOPfA06qtVxOA7AA+OBArSfwNuBe4ECKH5EOTeWv77sUVxMenMaHpuXU7NhbbV/IYaD4jd4iYCZwC8WP0Wt+7q0+ADsDj3XdF3P+fGoNTT8iY3Dcemf3iFgHkP7ulsqzr3vqQpsOLGWA1TN1L90PrAcWAr8BNkTE5rRIuR6v1zHN3wjssnUjbq5e7gs5uBg4B3gtTe9C/c+91e0JdABXpq7SyyTtQN6fz1u0QiLr1S2tBqis6y5pR+BG4KyIeKG7RWuUtXw9I+LViJhG8Q39AGBqrcXS3yzr2ChbsC+0NEnHAOsjYlm5uMaiuXy2Q4H9gUsiYjrwErl3I9bQComsV7e0ytwzksYApL/rU3m2dZc0jOIf17URcVMqHnD1BIiIDcBiinNAIyR1/v6yXI/X65jmvx14butG2hxbuC+0ukOAYyU9DvyQonvxYup/7q2uHWiPiKVpej5FYsv186mpFRLZYLj1zs3ArDQ+i+I8Qmf5qemqvoOAjZ2H+61MkoDLgRUR8Y3SrAFTT0mjJY1I48OBIyhOkt8JnJAW61rHzrqfANwR6QTEQNaHfaGlRcTciBgfEZMo/hfdEREnU/9zb2kR8TSwRtJeqehwikf/ZPn51NXsk3SprR8F/JriHMQ/NDueinW5DlgH/JHi29Bsij72RcCq9HdUWlYUV2z+BvgVMKPZ8feyjn9K0bXyIHB/Go4aSPUE3gPcl+r4EPDFVL4ncA+wGvgvYLtUvn2aXp3m79nsOrTivpDTABwG3NLd557DQHHVbVv6jP4bGDkQPp/y4Dt7mJlZ1lqha9HMzKzPnMjMzCxrTmRmZpY1JzIzM8uaE5mZmWXNiczMzLLmRGZmZllzIjMzs6z9PyfNAJMrekgcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ea00128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e2159b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286fee0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ffabef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QNS9JQ1KXNhuBhcAvgC0R0ZOqVH10g5mZWTs0lLwi4s2ImEzWy/VU4I+qVau2bL+ePWRmZtaAft1tGBFbgPuBQ8me8trbN2LNRzf42UNmZtZqjdxtOFrSHml8N+BIskeY3weclKrNBO5oV5BmZmZ5jfQqPwaYK2kIWbK7NSLukvQ0cIukS4D/JHtWkZmZWdvVTV4R8SRwcJXyNWTXv8zMzArlHjbMzKx0nLzMzKx0nLzMCiDpekkbJS2vMX+apFckLU3D+UXHaFYmjdywYWbNmwNcBdzQR50HI+L4YsIxKzcfeZkVICIeADZ3Og6zwcLJy6x7HJae3vAjSQfWquRea8ycvMy6xRPAvunpDVcCP6xV0b3WmDl5mXWFiNja+/SGiFgADJU0qsNhmXUtJy+zLiDpfZKUxqeStc2XOhuVWffy3YZmBZB0MzANGCVpHXAB2bPxiIhryfoJPUtSD/AbYEZEVH1Sg5k5eZkVIiJOqTP/KrJb6c2sAT5taGZmpePkZWZmpePkZWZmpePkZWZmpePkZWZmpePkZWZmpePkZWZmpePkZWZmpePkZWZmpePkZWZmpVM3eUkaL+k+SSslrZD0pVQ+UtJCSavS64j2h2tmZtbYkVcP8OWI+CPgUODzkg4AzgXujYhJwL1p2szMrO3qJq+I2BART6TxV4GVwFjgBGBuqjYXOLFdQZqZmeX165qXpInAwcAiYO+I2ABZggP2qrGMH1luZmYt1XDykjQc+D5wdkRsbXQ5P7LczMxaraHkJWkoWeK6KSJ+kIpfkDQmzR8DbGxPiGZmZttr5G5DAdcBKyPi8tys+cDMND4TuKP14ZmZme2okSOvw4FTgSMkLU3DccA3gKMkrQKOStNmVoWk6yVtlLS8xnxJ+pak1ZKelPShomM0K5Nd6lWIiIcA1Zg9vbXhmA1ac4CrgBtqzD8WmJSGQ4Br0quZVTGoetiQmhvM2iUiHgA291HlBOCGyDwK7NF7TdnMdjSokpdZiY0F1uam16WyHfjnJ1aoLj0qcPIy6w7VWnlUq+ifn5g5eZl1i3XA+Nz0OGB9h2Ix63pOXmbdYT7w2XTX4aHAK7092JjZjurebWhmzZN0MzANGCVpHXABMBQgIq4FFgDHAauBXwOndyZSs3Jw8jIrQEScUmd+AJ8vKByz0vNpQzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx0nLzMzKx33bdhFWvHctqj6BCgzs8HFR15mZlY6dZOXpOslbZS0PFc2UtJCSavS64j2hmlmZrZNI0dec4BjKsrOBe6NiEnAvWm6aVJzg5mZ7RzqJq+IeADYXFF8AjA3jc8FTmxxXGZmZjUN9JrX3r2PKE+ve9WqKOlMSYslLd60adMA386s/CQdI+kZSasl7XC2QtJpkjZJWpqGz3UiTrMyaPsNGxExKyKmRMSU0aNHt/vtzLqSpCHA1cCxwAHAKZIOqFJ1XkRMTsPsQoM0K5GBJq8XJI0BSK8bWxeS2aA0FVgdEWsi4nfALWSn381sAAaavOYDM9P4TOCO1oRjNmiNBdbmptelskp/LulJSbdJGl9MaGbl08it8jcDjwAfkLRO0hnAN4CjJK0CjkrTZlZbtfthK39SficwMSL+BPh3tt0Utf2KfB3ZrH4PGxFxSo1Z01sci9lgtg7IH0mNA9bnK0TES7nJfwEuq7aiiJgFzAKYMmWK+1SxnZJ72Bhk/Fu5rvU4MEnSfpKGATPITr+/rfc6cvJJYGWB8ZmVivs2NCtARPRI+gJwDzAEuD4iVki6GFgcEfOBL0r6JNBD9tvK0zoWsFmXc/IyK0hELAAWVJSdnxs/Dziv6LjMysinDc3MrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHScvMzMrHTcMa9tp9nHooSfLmVmBfCRl5mZlY6Tl5mZlY5PG1pL+bSjmRXBR15mZlY6TSUvScdIekbSaknntioos8GoXnuRtKukeWn+IkkTi4/SrBwGnLwkDQGuBo4FDgBOkXRAqwIzGwipuaF9cTXUXs4AXo6I/YErgMvaF5FZuTVz5DUVWB0RayLid8AtwAmtCcts0GmkvZwAzE3jtwHTpXamVLPyaiZ5jQXW5qbXpTIz21Ej7eXtOhHRA7wC7FlIdGYl08zdhtW+Ee5wr5ikM4Ez0+Rrkp5p4j3bZRTwYqeD6AId3w6dPs6Q6m6DfQe66iplle2l3W2q/3/fYv8gHd//qsq2QXfGtk33xld/+w2oTTWTvNYB43PT44D1lZUiYhYwq4n3aTtJiyNiSqfj6DRvh7Zug0baS2+ddZJ2Ad4LbK5c0UDbVLf/fbs5vm6ODXbO+Jo5bfg4MEnSfpKGATOA+a0Jy2zQaaS9zAdmpvGTgJ9G+JdvZtUM+MgrInokfQG4BxgCXB8RK1oWmdkgUqu9SLoYWBwR84HrgBslrSY74prRuYjNultTPWxExAJgQYti6aSuPq1ZIG+HNm6Dau0lIs7Pjf8W+FS73p/u//t2c3zdHBvshPHJZyXMzKxs3D2UmZmVzqBMXpKek/SUpKWSFqeykZIWSlqVXkfUWPbHkrZIuquifL/UZc+q1IXPsCI+y0C1aRvMkfRsWudSSZOL+CzNGOh2kDRZ0iOSVkh6UtLJuXkd3xea/PvOTHVWSZqZKz85fdYVkr7Zwfja3gbbFN8XlHXtFZJGDTS2NsZ3k7LuyZZLul7S0C6K7TpJy9L+d5uk4XUDiYhBNwDPAaMqyr4JnJvGzwUuq7HsdOATwF0V5bcCM9L4tcBZnf6cHdgGc4CTOv3ZitgOwPuBSWl8H2ADsEe37AtNfK6RwJr0OiKNjyD7MfSvgNGp3lxgepftfy3b7m2K72BgYrV1d0l8x5H9llDAzQPdfm2K7T258ct719VnHM1s4G4damzcZ4AxaXwM8Ewfy0/Lb9z0x34R2CVNHwbc0+nPWeQ2SGVzGBzJq+HtkFtmGTCpW/aFgX4u4BTgO7np76SyDwP/nis/Ffh2t+x/rd7u7Wgffa27m+JL8/8euLTbYkt/52uAc+rFMShPG5L1SvATSUuU9UYAsHdEbABIr3v1Y317Alsi67IHytEVVqu3Qa9L06H9FZJ2bVWwbdT0dpA0FRgG/ILu2RcG+rlqdVO1GvigpInKfiB9Itv/qLqo+Gpp9XZvV/tolbbFl04Xngr8uJtik/Rd4Hngg8CV9eoP1odRHh4R6yXtBSyU9PMm19dQtz1dptXbAOA8sp1rGNmtr+cAF7dgve3U1HaQNAa4EZgZEW9JVftL6sS+MNDPVTX+iHhZ0lnAPOAt4GHgDzoQXy2t3u7taB+t1M74vg08EBEPdlNsEXG6sqcvXAmcDHy3r/qD8sgrItan143A7WQ9er+Q/hH1/kPa2I9Vvgjskb6RQo2usLpJG7YBEbEhMm+Q7VhTWxt16zWzHSS9B7gb+HpEPJqKu2JfaOJz1eymKiLujIhDIuIwstNAqzoQXy0t3e7taB+t1K74JF0AjAb+odtiS+t8k+wL1J/Xqzvokpekd0vavXcc+DiwnO273pkJ3NHoOiM7GXsfWZc9/V6+aO3YBmldvTunyE4rLW9VzO3QzHZQdifb7cANEfFvveXdsC80+fe9B/i4pBHpjrCPpzLSN2lS+d8BszsQX1Wt3O7tah+t0sb2+zngaOCUiHirW2JTZv/ecbIbOuofzQ3kgl03D2SnOpalYQXwv1L5nsC9ZN8m7wVGpvIpwOzc8g8Cm4DfkH1LPTq33sfIrg38G7Brpz9rB7bBT4GnyHbW7wHDO/1Z27UdgL8Efg8szQ2Tu2FfaMHf969S7KuB03PlNwNPp2FGF+5/LdnubYzvi2m6h+yocHaXxddDdt22d38+vxtiIzuI+g+2/W+5idzdh7UG97BhZmalM+hOG5qZ2eDn5GVmZqXj5GVmZqXj5GVmZqXj5GVmZqXj5GVmZqXj5GVmZqXj5GVmZqXz/wGyrhzs+yYHbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ff6e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ff74b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228720cb748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22871fb9978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22872274438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228722f1080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DNzMz60Q7e4ILaP/5ZkeQniY/EzgJ+EZ3wjQzM+u+dh6qO5jnm80BLo1kCTB+oOFfMzOz4Wao5wQbPd9sKrCqMN7q3G8zfvaZmZmVrdsXxqhOv6g3op99ZmZmZRtqEmz0fLPVwPTCeNOANUMPz8zMrHeGmgQbPd/sWuBD+SrRA4DnBw6bmpmZDTctH6WUn292EDBJ0mrgs6TnmV2dn3X2BPD+PPp1wJHASuBl4IQexGxmZtYVLZPgYJ5vFhEBnNJpUGZmZv3gFmPMzKyynATNRghJ/1vSvZLukXSFpK3LjslspHMSNBsBJE0FPgbMjoi9gTHA3HKjMhv5nATNRo6xwDaSxgLb4tuPzDrmJGg2AkTEz4GvkK7GXku6/eiG4jhuhcls8JwEzUaA/KSWOcBuwM7AdpKOK47jVpjMBs9J0GxkOBR4NCLWR8RrwL8CB5Yck9mI5yRoNjI8ARwgaVtJIt2ne3/JMZmNeE6CZiNARNwOLALuBO4m1d0LSw3KbBRo2WKMmQ0PEfFZUrOFZtYl3hM0M7PKchI0M7PKchI0M7PKchI0M7PK6igJ1mvQV9Jukm6XtELSVZK27FawZmZm3TTkJNikQd8vAudFxEzgWWB+NwI1MzPrtk4Ph9Y26LsWOJh0PxPAQuCYDudhZmbWE0NOgvUa9AXuAJ6LiI15tNXA1Hrfd2O/ZmZWtk4Oh27WoC9wRJ1Ro9733divmZmVrZPDoY0a9B2fD48CTMPPPDMzs2GqkyRYr0Hf+4CbgPflceYB13QWopmZWW90ck6wUYO+pwGnSloJ7ABc3IU4zczMuq6jBrQbNOj7CLB/J9M1MzPrB7cYY2ZmleUkaGZmleUkaGZmleUkaGZmleUkaDZCSBovaZGkByTdL+kPy47JbKTr6OpQM+urfwR+GBHvy09n2bbsgMxGOidBsxFA0puBdwDHA0TEq8CrZcZkNhr4cKjZyLA7sB74lqT/lnSRpO2KI7hRerPBcxI0GxnGAvsB34iItwO/AE4vjuBG6c0Gz0nQbGRYDazOzRVCarJwvxLjMRsVnATNRoCIeBJYJWnP3GugwXoz64AvjDEbOT4KXJ6vDH0EOKHkeMxGPCdBsxEiIpYDs8uOw2w06ehwaL2bdyVNlLRY0or8PqFbwZqZmXVTp+cEB27e/R3g94D7SVes3RgRM4EbqbmCzczMbLgYchIs3Lx7MaSbdyPiOWAOsDCPthA4ptMgzczMeqGTPcFGN+/uFBFrAfL7jl2I08zMrOs6SYItb95txq1bmJlZ2TpJgo1u3n1K0hSA/L6u3pfduoWZmZVtyEmwyc271wLzcr95wDUdRWhmZtYjnd4nWO/m3S2AqyXNB54A3t/hPMzMzHqioyTY5ObdQzqZrpmZWT+47VAzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0GzEULSmPzElh+UHYvZaOEkaDZyfJz04Goz6xInQbMRQNI04N3ARWXHYjaaOAmajQznA58Eft1oBD+j02zwnATNhjlJRwHrIuKOZuP5GZ1mg+ckaDb8/RFwtKTHgCuBgyVdVm5IZqNDx0mw9oo1SbtJul3SCklX5WcNmtkQRcQZETEtImYAc4EfR8RxJYdlNip0Y0+w9oq1LwLnRcRM4FlgfhfmYWZm1nUdJcHaK9YkCTgYWJRHWQgc08k8zOwNEXFzRBxVdhxmo0Wne4K1V6ztADwXERvz59XA1Hpf9JVsZmZWtiEnwQZXrKnOqFHv+76SzczMyja2g+8OXLF2JLA18GbSnuF4SWPz3uA0YE3nYZqZmXXfkPcEG1yx9kHgJuB9ebR5wDUdR2lmZtYDvbhP8DTgVEkrSecIL+7BPMzMzDrWyeHQ34iIm4Gbc/cjwP7dmK6ZmVkvucUYMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBMzOrLCdBsxFA0nRJN0m6X9K9kj5edkxmo0FX2g41s57bCHwiIu6UNA64Q9LiiLiv7MDMRjLvCZqNABGxNiLuzN0vAvcDU8uNymzkcxI0G2EkzQDeDtxebiRmI9+Qk2CjcxSSJkpaLGlFfp/QvXDNqk3S9sB3gb+JiBdqhp0kaZmkZevXry8nQLMRppM9wYFzFG8DDgBOkTQLOB24MSJmAjfmz2bWIUlvIiXAyyPiX2uHR8SFETE7ImZPnjy5/wGajUBDToJNzlHMARbm0RYCx3QapFnVSRJwMXB/RPxD2fGYjRZdOSdYc45ip4hYCylRAjs2+I4P3Zi174+AvwAOlrQ8v44sOyizka7jWyRqz1GkDdbWIuJC4EKA2bNnR6dxmI1mEfGfQHuVy8za1tGeYINzFE9JmpKHTwHWdRaimZlZb3RydWijcxTXAvNy9zzgmqGHZ2Zm1judHA4dOEdxt6Tlud+ngHOBqyXNB54A3t9ZiGZmZr0x5CTY4hzFIUOdrpmZWb+4xRgzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6ssJ0EzM6usjp8naDZStPmoy4bCT700G3W8J2hmZpXVsyQo6XBJD0paKen0Xs3HrCpcp8y6rydJUNIY4J+BI4BZwLGSZvViXmZV4Dpl1hu92hPcH1gZEY9ExKvAlcCcHs3LrApcp8x6oFdJcCqwqvB5de5nZkPjOmXWA726OrTedXibXFsn6STgpPzxJUkPNpneJGDDkIPp8KrANnUUYx84vg5JLWPctZezr9OvtDrVJ8M9xs7i6/2KabiXH7SOsZd1CuhdElwNTC98ngasKY4QERcCF7YzMUnLImJ298LrvuEeo+PrXMkxuk4NM46vc8Mhxl4dDv0pMFPSbpK2BOYC1/ZoXmZV4Dpl1gM92ROMiI2S/hr4D2AMcElE3NuLeZlVgeuUWW/0rMWYiLgOuK5Lk2vrEE/JhnuMjq9zpcboOjXsOL7OlR6jwm1BmZlZRbnZNDMzq6zSk2CrpqAkbSXpqjz8dkkzCsPOyP0flHRYSfGdKuk+SXdJulHSroVhr0tanl89u4ihjRiPl7S+EMuHC8PmSVqRX/NKiu+8QmwPSXquMKznZSjpEknrJN3TYLgkfTXHf5ek/QrDel5+QyVpoqTFObbFkiY0GK9uGeeLcG7P378qX5DT1/gk7SvpNkn35rL/88KwBZIeLcS+bxdj83qpt/GVuk7aRESU9iKd4H8Y2B3YEvgZMKtmnI8AF+TuucBVuXtWHn8rYLc8nTElxPenwLa5+68G4sufXxomZXg88LU6350IPJLfJ+TuCf2Or2b8j5Iu+uhnGb4D2A+4p8HwI4HrSffqHQDc3q/y6/B3fQk4PXefDnyxwXh1yxi4Gpibuy8A/qrf8QG/DczM3TsDa4Hx+fMC4H09KDevl3ofX2nrpNpX2XuC7TQFNQdYmLsXAYdIUu5/ZUS8EhGPAivz9PoaX0TcFBEv549LSPdv9VMnzWkdBiyOiGci4llgMXB4yfEdC1zR5RiaiohbgWeajDIHuDSSJcB4SVPoT/l1olh3FgLHtPvFXMcOJtW5QX+/TS3ji4iHImJF7l4DrAMmdzmOWl4v9Ti+Jvpep8pOgu00BfWbcSJiI/A8sEOb3+1HfEXzSXsMA7aWtEzSEkndXoEMaDfGP8uHRhZJGrjpeliVYT5ksxvw40LvfpRhK41+w3BvymyniFgLkN93bDBevTLeAXgu1znozW9rNz4AJO1P2rN4uND7C/l/fZ6krboUl9dL/YmvrHXSJsp+qG7LpqCajNPOdzvV9jwkHQfMBt5Z6L1LRKyRtDvwY0l3R8TD9b7f4xi/D1wREa9IOpm0BXtwm9/tR3wD5gKLIuL1Qr9+lGErZf4Hm5L0I+CtdQadOYjJbFbGwAt1xhv0b+tSfOQ9728D8yLi17n3GcCTpMR4IXAacNZgY6w3uzr9vF7qbnxlrpM2UfaeYMumoIrjSBoLvIV06Kqd7/YjPiQdSqrUR0fEKwP98+EbIuIR4Gbg7V2Or60YI+LpQlzfBH6/3e/2I76CudQcCu1TGbbS6Df0o/yaiohDI2LvOq9rgKdy8hhIIusaTKNeGW8gHfYd2FAe0m/rRnyS3gz8O/DpfDh6YNpr8yHqV4Bv0b3Djl4v9Ti+ktdJm+rlCcdWL9Ke6COkQ2ADJ1D3qhnnFDY9AX117t6LTU9AP0L3T0C3E9/bSYdnZtb0nwBslbsnAStockFIj2OcUuh+L7Ak3jgJ/WiOdULuntjv+PJ4ewKPke9d7WcZ5unPoPGFMe9m0wtjlvar/Dr8TV9m0wtPvlRnnIZlDPwLm14Y85ES4tsSuBH4mzrDpuR3AecD5/brP+v1UsfxlbZO2izeXk68zQI7EngoL7Azc7+zSFsvAFvnyrgSWArsXvjumfl7DwJHlBTfj4CngOX5dW3ufyBwd/4D3A3ML7EMzwHuzbHcBPxO4bsn5rJdCZxQRnz58+dqV2L9KkPS3uda4DXSluh84GTg5DxcpAfaPpzjmN3P8uvgd+1ASiAr8vvE3H82cFGrMiZd3bc0/7Z/Ia88+xzfcXm5LC+89s3Dfpxjvge4DNi+X/9ZvF7qNL5S10nFl1uMMTOzyir7nKCZmVlpnATNzKyynATNzKyynATNzKyynATNzKyynATNzKyynATNzKyynATNzKyy/j8TpTaWmvWRJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2287233f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ff8a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c0vKNGzeWDNHMzKyoYolW0gLgOODtEdFIpmuBPSu9zQLWNQ8bERdGxNyImDtt2rRSIZqZmRVXJNFKOho4HXhzRDxd6XQVMF/S9pL2BuYAt5SIwczMrB+Mxu09S4CbgP0krZW0EDgfmAQslXS7pAsAIuJu4HLgHuAHwKkR8dxIYzDbFrS5lW6qpKWSVubvKXXGaGZbG42rjk+IiOkR8cKImBURF0XEyyJiz4g4KH9OqfT/6YjYNyL2i4hrOo3bzAZYxNa30p0BXBsRc4Br828z6yN+MpTZGNHqVjrSLXOLc/Ni4PieBmVmg3KiNRvb9oiI9QD5e/d2PfpqfrN6ONGabSN8Nb9ZPZxozca2RyRNB8jfG2qOx8yaONGajW1XAQty8wLgyhpjMbMWnGjNxog2t9J9FnijpJXAG/NvM+sjE+sOwMy6ExEntOl0RE8DMbMh8R6tmZlZQU60ZmZmBTnRmpmZFeREa2ZmVpATrZmZWUFOtGZmZgU50ZqZmRU0Gu+j7fodmUr+UdIqSXdIOnik0zczM+tno7FHu4ju35F5DDAnf04GvjwK0zczM+tbo/Hi96G8I3MecEkky4DJjQeim5mZjUelztG2e0fmTGBNpb+1ud0Afm+mmZmNF72+GEot2sVWLfzeTDMzGydKJdp278hcC+xZ6W8WsK5QDGZmZrUrlWjbvSPzKuCd+erjQ4EnGoeYzczMxqMRvyYvvyPzDcBuktYCnyC9E/Py/L7Mh4C35d6vBo4FVgFPA+8a6fTNzMz62YgT7VDekRkRAZw60mmamZmNFX4ylJmZWUFOtGbjgKT/KeluSXdJWiJph7pjMrPEidZsjJM0E/gfwNyIOBCYAMyvNyoza3CiNRsfJgI7SpoI7IRvmzPrG060ZmNcRPwG+ALpCv/1pNvmftjcn5+4ZlYPJ1qzMS6/HWsesDcwA9hZ0onN/fmJa2b1cKI1G/uOBB6IiI0R8UfgO8Bra47JzDInWrOx7yHgUEk7SRLpHvYVNcdkZpkTrdkYFxE3A1cAtwF3kur1hbUGZWbPG/GTocysfhHxCdLjT82sz3iP1szMrCAnWjMzs4KcaM3MzApyojUzMyuoaKJt9aBzSXtLulnSSkmXSdquZAxmZmZ1KpZoOzzo/Bzg3IiYAzwOLCwVg5mZWd1KHzpuftD5euBw0j1/AIuB4wvHYGZmVptiibbVg86BW4FNEbE597YWmNk8rB9+bmZm40XJQ8dbPegcOKZFr7FVCz/83MzMxomSh47bPeh8cj6UDDALvzfTzMzGsZKJttWDzu8BrgfemvtZAFxZMAYzM7NalTxH2+5B56cDH5G0CtgVuKhUDGZmZnUr+lKBNg86Xw0cUnK6ZmZm/cJPhjIzMyvIidbMzKwgJ1ozM7OCnGjNzMwKcqI1GwckTZZ0haR7Ja2Q9Jq6YzKzpOhVx2bWM18CfhARb81vxNqp7oDMLHGiNRvjJL0IeD1wEkBEPAs8W2dMZraFDx2bjX37ABuBr0v6haSvSdq5uSe/rMOsHk60ZmPfROBg4MsR8Srg98AZzT35ZR1m9XCiNRv71gJr82NPIT369OAa4zGzCidaszEuIh4G1kjaL7dqvMDDzPqAL4YyGx8+CHwzX3G8GnhXzfGYWeZEazYORMTtwNy64zCzrRU9dNzqJnpJUyUtlbQyf08pGYOZmVmdSp+jbdxE/wrgPwErSFdDXhsRc4BraXF1pJmZ2XhRLNFWbqK/CNJN9BGxCZgHLM69LQaOLxWDmZlZ3Uru0ba7iX6PiFgPkL93LxiDmZlZrUom2q5uom/FT7AxM7PxomSibXcT/SOSpgPk7w3NA/oJNmZmNl4US7QdbqK/CliQ2y0AriwVg5mZWd1K30fb6ib6FwCXS1oIPAS8rXAMZmZmtSmaaDvcRH9EyemamZn1Cz/r2MzMrCAnWjMzs4KcaM3MzApyojUzMyvIidbMzKwgJ1ozM7OCnGjNzMwKcqI1MzMryInWbJyQNCG/Ket7dcdiZls40ZqNHx8CVtQdhJkN5ERrNg5ImgW8Cfha3bGY2UBOtGbjw3nAR4E/tevB73k2q4cTrdkYJ+k4YENE3NqpP7/n2aweTrRmY9/rgDdLehC4FDhc0jfqDcnMGoom2uarICXtLelmSSslXZbfU2tmIxARZ0bErIiYDcwHrouIE2sOy8yy0nu0zVdBngOcGxFzgMeBhYWnb2ZmVqtiibb5KkhJAg4Hrsi9LAaOLzV9s21RRNwQEcfVHYeZbVFyj7b5KshdgU0RsTn/XgvMbDWgr440M7PxokiibXMVpFr0Gq2G99WRZmY2XkwsNN7GVZDHAjsALyLt4U6WNDHv1c4C1hWavpmZWV8oskfb5irItwPXA2/NvS0AriwxfTMzs37R6/toTwc+ImkV6ZztRT2evpmZWU+VOnT8vIi4AbghN68GDik9TTMzs37hJ0OZmZkV5ERrZmZWkBOtmZlZQU60ZmZmBTnRmpmZFeREa2ZmVpATrZmZWUFOtGZmZgU50ZqZmRXkRGtmZlaQE63ZGCdpT0nXS1oh6W5JH6o7JjPbovizjs2suM3AaRFxm6RJwK2SlkbEPXUHZmbeozUb8yJifUTclpufBFYAM+uNyswanGjNxhFJs4FXATfXG4mZNRRLtO3OG0maKmmppJX5e0qpGMy2JZJ2Af4V+HBE/K5F95MlLZe0fOPGjb0P0GwbVXKPtnHe6M+AQ4FTJe0PnAFcGxFzgGvzbzMbAUkvJCXZb0bEd1r1ExEXRsTciJg7bdq03gZotg0rlmg7nDeaByzOvS0Gji8Vg9m2QJKAi4AVEfHFuuMxs4F6co626bzRHhGxHlIyBnZv0b8PcZl173XAO4DDJd2eP8fWHZSZJcVv72k+b5T+fHcWERcCFwLMnTs3ykZoNrZFxE+AwSuWmdWi6B5tm/NGj0ianrtPBzaUjMHMzKxOJa86bnfe6CpgQW5eAFxZKgYzM7O6lTx03DhvdKek23O7jwGfBS6XtBB4CHhbwRjMzMxqVSzRDnLe6IhS0zUzM+snfjKUmZlZQU60ZmZmBTnRmpmZFeREa2ZmVpATrZmZWUFOtGZmZgU50ZqZmRXkRGtmZlaQE62ZmVlBTrRmZmYFOdGamZkVVPx9tGZjQRevSe4o/NZkM2vDe7RmZmYF1ZJoJR0t6T5JqySdUUcMZuOJ65RZ/+p5opU0Afhn4Bhgf+AESfv3Og6z8cJ1yqy/1bFHewiwKiJWR8SzwKXAvBriMBsvXKfM+lgdiXYmsKbye21uZ2bD4zpl1sfquOq41fWdA67ZlHQycHL++ZSk+wYZ527Ao8MOaIRXnHZhRPH1QL/HB30eozRofHuVnHyLdltdBz3EetXX5c14j68HGyXGfhmWrFOjqo5EuxbYs/J7FrCu2kNEXAhc2O0IJS2PiLmjE97oc3wj1+8x1hzfoHUKhlavXN4j0+/xQf/H2O/xDUUdh45/DsyRtLek7YD5wFU1xGE2XrhOmfWxnu/RRsRmSR8A/h2YAFwcEXf3Og6z8cJ1yqy/1fJkqIi4Grh6FEfZ9WHmmji+kev3GGuNz3Wq7/R7fND/MfZ7fF1T+NlxZmZmxfgRjGZmZgX1daId7LFykraXdFnufrOk2ZVuZ+b290k6qqb4PiLpHkl3SLpW0l6Vbs9Juj1/il240kWMJ0naWInlPZVuCyStzJ8FNcV3biW2X0naVOlWvAwlXSxpg6S72nSXpH/M8d8h6eBKt+LlN1ySpkpammNbKmlKm/5alnG+8OrmPPxl+SKsnsco6SBJN0m6O5f/X1W6LZL0QCX+g0YpLm+XysdY63Zp1EVEX35IF3XcD+wDbAf8Eti/qZ/3Axfk5vnAZbl5/9z/9sDeeTwTaojvvwI75eb3NeLLv5/qkzI8CTi/xbBTgdX5e0puntLr+Jr6/yDpQp9eluHrgYOBu9p0Pxa4hnQv66HAzb0qvxHO1+eAM3LzGcA5bfprWcbA5cD83HwB8L46YgReDszJzTOA9cDk/HsR8NZRjsnbpd7EWNt2qcSnn/dou3ms3DxgcW6+AjhCknL7SyPimYh4AFiVx9fT+CLi+oh4Ov9cRrq/sZdG8mi+o4ClEfFYRDwOLAWOrjm+E4AloxxDRxFxI/BYh17mAZdEsgyYLGk6vSm/kajWncXA8d0OmOvY4aQ6N+Thh2DQGCPiVxGxMjevAzYA0wrE0uDtUg9i7KDf61VL/Zxou3ms3PP9RMRm4Alg1y6H7UV8VQtJez4NO0haLmmZpBIbKeg+xr/Mh5GukNR48EFflWE+vLU3cF2ldS/KcDDt5qHfH4u4R0SsB8jfu7fpr1UZ7wpsynUOys1btzECIOkQ0h7S/ZXWn87r9rmSth+FmLxdGrl+3y6Nun5+8Xs3j5Vr109Xj6Qboa6nIelEYC5wWKX1SyNinaR9gOsk3RkR97cavnCM/wYsiYhnJJ1C+id+eJfD9iK+hvnAFRHxXKVdL8pwMHWugx1J+hHwkhadzhrCaLYqY+B3Lfob1ryNUozkowj/F1gQEX/Krc8EHiYl3wuB04GzhxNndVIt2nm7NPox1rldGnX9vEfbzWPlnu9H0kTgxaTDfF09kq4H8SHpSNJG480R8UyjfT7MRUSsBm4AXjXK8XUVY0T8thLXV4H/3O2wvYivYj5Nh417VIaDaTcPvSi/jiLiyIg4sMXnSuCRnJwaSWpDm3G0KuNHSYfIG3/Uhz1voxGjpBcB3wc+ng/fN8a9Ph/Sfwb4OqNzmNbbpR7EWPN2afTVfZK43Ye0t72adLiwccL8gKZ+TmXgRQeX5+YDGHjRwWpG/6KDbuJ7Fekw1pym9lOA7XPzbsBKOlwEVDjG6ZXm/wYsy81TgQdyrFNy89Rex5f72w94kHzfdy/LMI9/Nu0vhnoTAy+GuqVX5TfCefo8Ay80+lyLftqWMfBtBl4M9f6aYtwOuBb4cItu0/O3gPOAz/ZinfV2aVRirG27VOJTewCDLJBjgV/lleKs3O5s0r8wgB1yhV8F3ALsUxn2rDzcfcAxNcX3I+AR4Pb8uSq3fy1wZ17B7gQW1liGnwHuzrFcD7yiMuy7c9muAt5VR3z59981byR7VYakvej1wB9J/6YXAqcAp+TuIr10/f4cx9xelt8I5mtXUoJamb+n5vZzga8NVsakK0ZvyfP2bfIGuoYYT8zL5vbK56Dc7boc913AN4BderHO4u3SaMRY63ZptD9+MpSZmVlB/XyO1szMbMxzojUzMyvIidbMzKwgJ1ozM7OCnGjNzMwKcqI1MzMryInWzMysICdaMzOzgv4/wtKHO+Rr6QwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ea002b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286fb22390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e0c0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cBIwD5pYbldlgcwI0GxzjgR0kjQd2pEEvS2bWHCdAswEQEU8AnyfreGIjWS9L38lP4/51zVrjBGg2ANJDp+cAM4C9gJ0knZqfxv3rmrXGCdBsMBwNrIuIzRHxC+CbwG+WHJPZQHMCNBsMjwOHSdpRksi6Inyw5JjMBpoToNkAiIg7gBuBu4H7yNruwlKDMhtwo3aGbWb9ISIuJnsai5l1gPcAzcyskpwAzcyskgolwHpdM0maIekOSQ9LWiJpu04Fa2Zm1iltJ8ARumb6DHB5RMwEngUWdCJQMzOzTip6CLS2a6aNwJFkV6sBLAZOKrgOMzOzjms7Adbrmgm4C9gSEa+myYaAqfXmd7dNZmZWpiKHQN/UNRNwXJ1Jo9787rbJzMzKVOQQaKOumSamQ6IA03CP9WZm1oeKJMB6XTP9BLgV+FCaZh5wU7EQzczMOq/IOcBGXTOdD5wraQ2wG3BNB+I0MzPrqEJdoTXommktcGiR5ZqZmXWbe4IxM7NKcgI0M7NKcgI0M7NKcgI0M7NKcgI0M7NKcgI0M7NKcgI0GxCSJkq6UdJPJT0o6TfKjslskBW6D9DMeupvgf8bER9Kz9ncseyAzAaZE6DZAJD0FuC3gPkAEfEK8EqZMZkNOh8CNRsM+wGbgS9L+jdJV0vaKT+BHzFm1honQLPBMB44BLgyIt4FvAhckJ/Ajxgza40ToNlgGAKGUif0kHVEf0iJ8ZgNPCdAswEQEU8C6yXtn0YNP37MzNrki2DMBsc5wNfSFaBrgdNKjsdsoBVKgJImAlcDBwEBnA6sBpYA04FHgd+NiGcLRWlmRMQ9wOyy4zAbK4oeAh2+L+kA4NeBB8lOzK+IiJnACmpO1JuZmfWDthNg7r6kayC7LykitgBzgMVpssXASUWDNDMz67Qie4CN7kvaMyI2AqS/e9Sb2fcsmZlZmYokwFHvSxqJ71kyM7MyFUmAje5LekrSFID0d1OxEM3MzDqv7QQ4wn1Jy4B5adw84KZCEZqZmXVB0fsA692XtA2wVNIC4HHgwwXXYWZm1nGFEuAI9yUdVWS5ZmZm3eau0MzMrJKcAM3MrJKcAM3MrJKcAM3MrJKcAM3MrJKcAM3MrJKcAM3MrJKcAM3MrJKcAM3MrJKcAM0GhKRx6dFj/1R2LGZjgROg2eD4E+DBsoMwGyucAM0GgKRpwAnA1WXHYjZWOAGaDYYrgPOAXzaaQNIZklZJWrV58+beRWY2oJwAzfqcpA8AmyLirpGmi4iFETE7ImZPnjy5R9GZDa7CCbD2xLykGZLukPSwpCXpWYFm1r73AidKehS4AThS0lfLDcls8HViD7D2xPxngMsjYibwLLCgA+swq6yIuDAipkXEdGAucEtEnFpyWGYDr1ACrD0xL0nAkcCNaZLFwElF1mFmZtYNRfcAa0/M7wZsiYhX0/shYGq9GX3C3qx1EXFbRHyg7DjMxoK2E2CDE/OqM2nUm98n7M3MrEzjC8w7fGL+eGB74C1ke4QTJY1Pe4HTgA3FwzQzM+ustvcAG5yYPwW4FfhQmmwecFPhKM3MzDqsG/cBng+cK2kN2TnBa7qwDjMzs0KKHAJ9XUTcBtyWhtcCh3ZiuWZmZt3inmDMzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNzKySnADNBoCkvSXdKulBSQ9I+pOyYzIbdB3pDNvMuu5V4M8i4m5JuwB3SVoeET8pOzCzQeU9QLMBEBEbI+LuNPw88CAwtdyozAZb2wmw0SEZSbtKWi7p4fR3UufCNTNJ04F3AXeUG4nZYCuyBzh8SOadwGHA2ZJmARcAKyJiJrAivTezDpC0M/AN4OMR8bOasjMkrZK0avPmzeUEaDZA2k6AIxySmQMsTpMtBk4qGqSZgaRtyZLf1yLim7XlEbEwImZHxOzJkyf3PkCzAdORc4A1h2T2jIiNkCVJYI9OrMOsyiQJuAZ4MCL+pux4zMaCwglwpEMyo8znwzVmzXsv8PvAkZLuSa/jyw7KbJAVug2iwSGZpyRNiYiNkqYAm+rNGxELgYUAs2fPjiJxmI11EfF9QGXHYTaWFLkKtNEhmWXAvDQ8D7ip/fDMzMy6o8ge4PAhmfsk3ZPGfQK4DFgqaQHwOPDhYiGamZl1XtsJcJRDMke1u1wzM7NecE8wZmZWSU6AZmZWSU6AZmZWSU6AZmZWSU6AZmZWSU6AZmZWSU6AZmZWSU6AZmZWSU6AZmZWSYU6wzYbJCrYlXS4y3azMcV7gGZmVklOgGZmVklOgGZmVklOgGZmVkldS4CSjpW0WtIaSRd0az1mVeE2ZdZZXUmAksYB/xs4DpgFnCxpVjfWZVYFblNmndetPcBDgTURsTYiXgFuAOZ0aV1mVeA2ZdZh3UqAU4H1ufdDaZyZtcdtyqzDunUjfL1bjre6jVjSGcAZ6e0LklaPsLzdgafbDqbgDdBNKhRjDzi+gqRRY9y3m6uvM660NtUj/R5jsfi6/8PU7/UHo8fYzTbVtQQ4BOydez8N2JCfICIWAgubWZikVRExu3PhdV6/x+j4iis5RrepPuP4iis7xm4dAv1XYKakGZK2A+YCy7q0LrMqcJsy67Cu7AFGxKuS/gj4f8A44NqIeKAb6zKrArcps87rWmfYEXEzcHOHFtfUYZ2S9XuMjq+4UmN0m+o7jq+4UmNUuIt7MzOrIHeFZmZmlVR6AhyteydJEyQtSeV3SJqeK7swjV8t6ZiS4jtX0k8k3StphaR9c2WvSbonvbp2wUITMc6XtDkXyx/kyuZJeji95pUU3+W52B6StCVX1vU6lHStpE2S7m9QLkl/l+K/V9IhubKu11+7JO0qaXmKbbmkSQ2mq1vH6YKbO9L8S9LFNz2NT9LBkn4k6YFU9x/JlS2StC4X+8EdjM2/S92Nr9TfpNdFRGkvspP5jwD7AdsBPwZm1UzzMeCqNDwXWJKGZ6XpJwAz0nLGlRDfbwM7puGzhuNL71/okzqcD3yhzry7AmvT30lpeFKv46uZ/hyyCzx6WYe/BRwC3N+g/Hjg22T34h0G3NGr+iv4uT4LXJCGLwA+02C6unUMLAXmpuGrgLN6HR/wDmBmGt4L2AhMTO8XAR/qQr35d6n78ZX2m5R/lb0H2Ez3TnOAxWn4RuAoSUrjb4iIlyNiHbAmLa+n8UXErRHxUnq7kuz+rF4q0kXWMcDyiHgmIp4FlgPHlhzfycD1HY5hRBHxPeCZESaZA1wXmZXARElT6E39FZFvO4uBk5qdMbWxI8naXMvzN2nU+CLioYh4OA1vADYBkzscRy3/LnU5vhH0tE2VnQCb6d7p9Wki4lXgOWC3JuftRXx5C8j2FIZtL2mVpJWSOv3jMazZGP9rOhxyo6ThG6r7qg7TYZoZwC250b2ow9E0+gz93j3ZnhGxESD93aPBdPXqeDdgS2pz0J3P1mx8AEg6lGyP4pHc6EvTdn25pAkdisu/S72Jr6zfpNd17TaIJo3avdMI0zQzb1FNr0PSqcBs4PDc6H0iYoOk/YBbJN0XEY/Um7/LMf4jcH1EvCzpTLL/XI9sct5exDdsLnBjRLyWG9eLOhxNmdvgiCR9F3hbnaKLWljMm+oY+Fmd6Vr+bB2Kj7TH/RVgXkT8Mo2+EHiSLCkuBM4HPtVqjPVWV2ecf5c6G1+Zv0mvK3sPcNTunfLTSBoPvJXscFUz8/YiPiQdTdagT4yIl4fHp0M2RMRa4DbgXR2Or6kYI+Lfc3F9CfhPzc7bi/hy5lJz+LNHdTiaRp+hF/U3oog4OiIOqvO6CXgqJY7hBLKpwTLq1fHTZId6h/9JbuuzdSI+SW8B/hn4ZDoEPbzsjemw9MvAl+ncoUb/LnU5vpJ/k7YKpLQX2R7oWrLDXsMnSw+smeZstj7ZvDQNH8jWJ5vX0vmTzc3E9y6yQzIza8ZPAiak4d2Bhxnh4o8uxzglN/w7wMp444TzuhTrpDS8a6/jS9PtDzxKuje1l3WYlj+dxhfBnMDWF8Hc2av6K/iZPsfWF5l8ts40DesY+DpbXwTzsRLi2w5YAXy8TtmU9FfAFcBlvdpm/btUOL7SfpO2iqNbC26hso4HHkpf1kVp3KfI/msB2D41xDXAncB+uXkvSvOtBo4rKb7vAk8B96TXsjT+N4H70pd/H7CgxDr8NPBAiuVW4IDcvKenul0DnFZGfOn9JbU/YL2qQ7K9zo3AL8j+A10AnAmcmcpF9jDaR1Ics3tZfwU+125kyePh9HfXNH42cPVodUx2Fd+d6bN9nfTD2eP4Tk3fyz2518Gp7JYU8/3AV4Gde7XN4t+lovGV+ps0/HJPMGZmVkllnwM0MzMrhROgmZlVkhOgmZlVkhOgmZlVkhOgmZlVkhOgmZlVkhOgmZlVkhOgmZlV0v8Hr6uol4UT9h4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286fad45f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2287221d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df2ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2287211bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df9b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2287005c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22871ff1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286fefd2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jR4RzaPyJ2YeQnko/lfRctAvbE6aZmVln1E2EI3xi9gzg8kgWAuOGHh9jZmbWj5q9RrjWE7OBoSdmTwKWFcZbnvutQ9LxkhZJWrR69eomwzAzM2tNu2+WUZV+UW1EP03bzMz6QbOJsNYTs5cD2xfGmwysaD48MzOzzmo2EdZ6YvZ84JP57tF9gJeGTqGamZn1o7oP5s1PzN4PmCBpOXAm6QnZ8/LTs58GPp5Hvw44FFgCvAYc24GYzczM2qZuIhzJE7MjIoCTWg3KzNYl6XPAp0jX3e8Hjo2I/+xtVGajn1uWMRsFJE0C/jswPSL2ANYHZvY2KrPB4ERoNnqMATaSNAbYGN+IZtYWToRmo0BE/Br4Buma/ErSjWg3FMfxf3PNmuNEaDYK5PZ8ZwA7AdsBm0g6ujiO/5tr1hwnQrPR4UDgiYhYHRG/B34EvL/HMZkNBCdCs9HhaWAfSRtLEumu7Yd7HJPZQHAiNBsFIuJO4BrgHtJfJ9YDLu5pUGYDou7/CM2sP0TEmaQGLcysjXxEaGZmpeZEaGZmpeZEaGZmpeZEaGZmpeZEaGZmpdZSIpT0OUkPSnpA0pWSNpS0k6Q7JS2WdLWkDdoVrJmZWbs1nQiHaQ3/q8D5ETEVeAGY3Y5AzczMOqHVU6OVreGvBPYn/fEXYC5wRIvLMDMz65imE2G11vCBu4EXI2JNHm05MKna9G4p38zM+kErp0bXaQ0fOKTKqFFtereUb2Zm/aCVU6O1WsMfl0+VAkzGDw81M7M+1koirNYa/kPALcDH8jizgGtbC9HMzKxzWrlGWKs1/FOBz0taAmwFXNqGOM3MzDqipadP1GgNfymwdyvzNTMz6xa3LGNmZqXmRGhmZqXmRGhmZqXmRGg2SkgaJ+kaSY9IeljS+3odk9kgaOlmGTPrqv8D/DwiPpYbs9+41wGZDQInQrNRQNLmwIeAYwAi4nfA73oZk9mg8KlRs9FhZ2A18D1J/y7pEkmbFEdw+71mzXEiNBsdxgDTgAsjYi/gVeC04ghuv9esOU6EZqPDcmB5btEJUqtO03oYj9nAcCI0GwUi4hlgmaRdcq+htn3NrEW+WcZs9DgZuCLfMboUOLbH8ZgNBCdCs1EiIu4Fpvc6DrNB41OjZmZWai0lwmotXUjaUtICSYvz+/h2BWtmZtZurR4RDrV0sSvwR8DDpFu6b4qIqcBNVNzibWZm1k+aToSFli4uhdTSRUS8CMwA5ubR5gJHtBqkmZlZp7RyRFirpYttImIlQH7fug1xmpmZdUQribBuSxfDcXNQZmbWD1pJhLVaunhW0rYA+X1VtYndHJSZmfWDphPhMC1dzAdm5X6zgGtbitDMzKyDWv1DfbWWLtYD5kmaDTwNfLzFZZiZmXVMS4lwmJYuDmhlvmZmZt3ilmXMzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNRglJ6+cG7n/W61jMBokTodno8dekZ36aWRs5EZqNApImAx8BLul1LGaDxonQbHS4ADgF+EOtEfxoM7PmOBGa9TlJhwGrIuLu4cbzo83MmuNEaNb/PgAcLulJ4Cpgf0k/6G1IZoPDidCsz0XE6RExOSKmADOBmyPi6B6HZTYwWk6Elbd0S9pJ0p2SFku6Oj+r0MzMrC+144iw8pburwLnR8RU4AVgdhuWYWZARNwaEYf1Og6zQdJSIqy8pVuSgP2Ba/Ioc4EjWlmGmZlZJ7V6RFh5S/dWwIsRsSZ/Xg5Mqjahb/U2M7N+0HQirHFLt6qMGtWm963eZmbWD8a0MO3QLd2HAhsCm5OOEMdJGpOPCicDK1oP08zMrDOaPiKscUv3UcAtwMfyaLOAa1uO0szMrEM68T/CU4HPS1pCumZ4aQeWYWZm1hatnBp9U0TcCtyau5cCe7djvmZmZp3mlmXMzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNzKzUnAjNRgFJ20u6RdLDkh6U9Ne9jslsULSliTUz67g1wBci4h5JmwF3S1oQEQ/1OjCz0c5HhGajQESsjIh7cvcrwMPUeOi1mY2ME6HZKCNpCrAXcGdvIzEbDK08ob7qNQtJW0paIGlxfh/fvnDNyk3SpsA/AZ+NiJcrhh0vaZGkRatXr+5NgGajUCtHhEPXLN4N7AOcJGk34DTgpoiYCtyUP5tZiyS9jZQEr4iIH1UOj4iLI2J6REyfOHFi9wM0G6VaeUJ9rWsWM4C5ebS5wBGtBmlWdpJEesj1wxHx972Ox2yQtOUaYcU1i20iYiWkZAlsXWMan8Yxa9wHgE8A+0u6N78O7XVQZoOg5b9PVF6zSDuu9UXExcDFANOnT49W4zAbZBFxB9BY5TKzEWnpiLDGNYtnJW2bh28LrGotRDMzs85p5a7RWtcs5gOzcvcs4NrmwzMzM+usVk6NDl2zuF/Svbnfl4DzgHmSZgNPAx9vLUQzM7POaToR1rlmcUCz8zUzM+smtyxjZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal5kRoZmal1rFEKOlgSY9KWiLptE4tx6xRUmuvXnOdMuuMjiRCSesD/wAcAuwGHClpt04sy6wMXKfMOqdTR4R7A0siYmlE/A64CpjRoWWZlYHrlFmHdCoRTgKWFT4vz/3MrDmuU2Yd0vQT6uuodkUl1hpBOh44Pn/8raRHh5nfBOC5poPpzvWdlmLsAsfXIqlujDt2cvFV+vWsTnVJv8fYWnyd3zD1e/lB/Rg7Wafe1KlEuBzYvvB5MrCiOEJEXAxc3MjMJC2KiOntC6/9+j1Gx9e6HsfoOtVnHF/r+iXGTp0a/TdgqqSdJG0AzATmd2hZZmXgOmXWIR05IoyINZL+Cvh/wPrAZRHxYCeWZVYGrlNmndOpU6NExHXAdW2aXUOne3qs32N0fK3raYyuU33H8bWuL2JURNQfy8zMbEC5iTUzMyu1nifCes1GSRor6eo8/E5JUwrDTs/9H5V0UI/i+7ykhyTdJ+kmSTsWhr0h6d786tiNDQ3EeIyk1YVYPlUYNkvS4vya1aP4zi/E9pikFwvDOl6Gki6TtErSAzWGS9I3c/z3SZpWGNbx8muWpC0lLcixLZA0vsZ4Vcs435hzZ57+6nyTTlfjk7SnpF9KejCX/V8Uhs2R9EQh9j3bGJu3S52Nr6fbpHVERM9epIv+jwM7AxsAvwJ2qxjnM8BFuXsmcHXu3i2PPxbYKc9n/R7E9yfAxrn7xKH48uff9kkZHgN8q8q0WwJL8/v43D2+2/FVjH8y6UaQbpbhh4BpwAM1hh8KXE/6L98+wJ3dKr8Wv9fXgNNy92nAV2uMV7WMgXnAzNx9EXBit+MD3gVMzd3bASuBcfnzHOBjHSg3b5c6H1/PtknVXr0+Imyk2agZwNzcfQ1wgCTl/ldFxOsR8QSwJM+vq/FFxC0R8Vr+uJD0/65uaqXprYOABRHxfES8ACwADu5xfEcCV7Y5hmFFxO3A88OMMgO4PJKFwDhJ29Kd8mtFse7MBY5odMJcx/Yn1bkRT9+guvFFxGMRsTh3rwBWARPbHEclb5c6HN8welKnep0IG2k26s1xImIN8BKwVYPTdiO+otmkI4chG0paJGmhpHZvRIY0GuNH82mSayQN/TG7r8own77ZCbi50LsbZVhPre/Q782ebRMRKwHy+9Y1xqtWxlsBL+Y6B535bo3GB4CkvUlHGI8Xep+T1+vzJY1tU1zeLnUnvl5tk9bRsb9PNKhus1HDjNPItK1qeBmSjgamA/sWeu8QESsk7QzcLOn+iHi82vQdjvGnwJUR8bqkE0h7svs3OG034hsyE7gmIt4o9OtGGdbTy3VwWJJuBN5eZdAZI5jNOmUMvFxlvBF/tzbFRz4C/z4wKyL+kHufDjxDSo4XA6cCZ400xmqLq9LP26X2xtfLbdI6en1EWLfZqOI4ksYAW5BOYzUybTfiQ9KBpIp9eES8PtQ/n8ohIpYCtwJ7tTm+hmKMiN8U4vou8MeNTtuN+ApmUnFatEtlWE+t79CN8htWRBwYEXtUeV0LPJsTyFAiWVVjHtXK+DnSKeChneWmvls74pO0OfDPwJfzqemhea/Mp6tfB75H+05BervU4fh6vE1aV6cvQg73Ih2RLiWdDhu6qLp7xTgnsfZF6Xm5e3fWvii9lPZflG4kvr1Ip2qmVvQfD4zN3ROAxQxzk0iHY9y20P3nwMJ468L0EznW8bl7y27Hl8fbBXiS/N/WbpZhnv8Uat8s8xHWvlnmrm6VX4vf6eusfTPK16qMU7OMgR+y9s0yn+lBfBsANwGfrTJs2/wu4ALgvG6ts94utRxfz7ZJVWPu9AIaKLRDgcfyj3ZG7ncWaS8GYMNcIZcAdwE7F6Y9I0/3KHBIj+K7EXgWuDe/5uf+7wfuzyvB/cDsHpbhucCDOZZbgF0L0x6Xy3YJcGwv4sufv1K5IetWGZKOQlcCvyftkc4GTgBOyMNFeiju4zmO6d0svxa+11akJLI4v2+Z+08HLqlXxqS7/u7K3+2H5A1ol+M7Ov8u9xZee+ZhN+eYHwB+AGzarXUWb5daja+n26TKl1uWMTOzUuv1NUIzM7OeciI0M7NScyI0M7NScyI0M7NScyI0M7NScyI0M7NScyI0M7NScyI0M7NS+/8lnCkYgHSOugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ff92f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MzKzTGk6cEXFsnclXDFL+XODc4QRlZmbWrfzkIDMzsxKcOM3MzEpw4jQzMyvBidPMzKwEJ04zM7MSnDjNzMxKcOI0MzMrwYnTzMysBCdOMzOzEpw4zczMSnDiNDMzK8GJ08zMrAQnTjMzsxIaTpySrpT0kqTHCtO2lDRX0jP5/7g8XZIukrRY0iOS9m5H8GZmZp1W5ojzKuCwmmmnA3dExC7AHXkc4HDSj1fvAswELm4uTDMzs+7QcOKMiHuAVTWTpwKz8vAs4OjC9KsjmQ+MlbRts8GamZlVrdlrnB+KiJUA+f82efoEYFmh3PI8zczMrKe16+Yg1ZkWdQtKMyUtlLSwr6+vTeGYmZm1RrOJ88X+U7D5/0t5+nJg+0K5icCKeguIiEsjYkpETBk/fnyT4ZiZmbVXs4nzFmB6Hp4O3FyY/tl8d+2+wCv9p3TNzMx6WZmvo8wGfgrsKmm5pBnAecAnJD0DfCKPA9wGLAEWA5cBX2hp1GYjlKSxkuZIelLSIkkfqzoms5FmdKMFI+LYAWYdXKdsACcPNygzG9CFwI8i4hhJGwJjqg7IbKRpOHGaWbUkfRA4ADgeICLeAt6qMiazkciP3DPrHTsDfcB3Jf1M0uWSNq0t5DvVzdrLidOsd4wG9gYujoi9gNd4/2ld7/Gd6mbt5cRp1juWA8sjYkEen0NKpGbWQU6cZj0iIl4AlknaNU86GHiiwpDMRiTfHGTWW04Brsl31C4BPldxPGYjjhOnWQ+JiIeAKVXHYTaS+VStmZlZCU6cZmZmJThxmpmZleDEaWZmVoITp5mZWQlOnGZmZiW0JHFK+mtJj0t6TNJsSRtL2knSAknPSLo+f+/MzMyspzWdOCVNAP4KmBIRewCjgGnAN4HzI2IXYDUwo9m6zMzMqtaqU7WjgU0kjSb9PuBK4CDSszQBZgFHt6guMzOzyjSdOCPiF8C3gKWkhPkK8ACwJiLeycWWAxOarcvMzKxqrThVOw6YCuwEbAdsChxep2gM8Hr/dqCZmfWMVpyqPQR4LiL6IuJt4EZgP2BsPnULMBFYUe/F/u1AMzPrJa1InEuBfSWNkSTe/6mju4BjcpnpwM0tqMvMzKxSrbjGuYB0E9CDwKN5mZcCpwFflrQY2Aq4otm6zMzMqtaSnxWLiLOAs2omLwH2acXyzczMuoWfHGRmZlaCE6eZmVkJTpxmZmYlOHGamZmV4MRpZmZWghOnmZlZCU6cZmZmJThxmpmZleDEaWZmVoITp5mZWQlOnGY9RtIoST+TdGvVsZiNRE6cZr3nS8CiqoMwG6mcOM16iKSJwCeBy6uOxWykaknilDRW0hxJT0paJOljkraUNFfSM/n/uFbUZTbCXQCcCvy26kDMRqpWHXFeCPwoInYDfo90Gul04I6I2AW4I4+b2TBJOhJ4KSIeGKLcTEkLJS3s6+vrUHRmI0fTiVPSB4EDyD9UHRFvRcQaYCowKxebBRzdbF1mI9z+wFGSngeuAw6S9P3aQhFxaURMiYgp48eP73SMZuu9Vhxx7gz0Ad/Nd/pdLmlT4EMRsRIg/9+mBXWZjVgRcUZETIyIScA04M6I+HTFYZmNOK1InKOBvYGLI2Iv4DVKnJb1aSUzM+slrUicy4HlEbEgj88hJdIXJW0LkP+/VO/FPq1kVl5EzIuII6uOw2wkajpxRsQLwDJJu+ZJBwNPALcA0/O06cDNzdZlZmZWtdEtWs4pwDWSNgSWAJ8jJeUbJM0AlgKfalFdZmZmlWlJ4oyIh4ApdWYd3Irlm5mZdQs/OcjMzKwEJ04zM7MSnDjNzMxKcOI0MzMrwYnTzMysBCdOMzOzEpw4zczMSnDiNDMzK8GJ08zMrIRWPXLPzMxGIqm510e0Jo4O8hGnmZlZCU6cZmZmJThxmpmZldCyxClplKSfSbo1j+8kaYGkZyRdn39yzMzMrKe18ojzS8Ciwvg3gfMjYhdgNTCjhXWZmZlVoiWJU9JE4JPA5XlcwEHAnFxkFnB0K+oyMzOrUquOOC8ATgV+m8e3AtZExDt5fDkwoUV1mZmZVabpxCnpSOCliHigOLlO0bpf1pE0U9JCSQv7+vqaDcfMzKytWnHEuT9wlKTngetIp2gvAMZK6n/AwkRgRb0XR8SlETElIqaMHz++BeGYmZm1T9OJMyLOiIiJETEJmAbcGRHHAXcBx+Ri04Gbm63LzMysau38HudpwJclLSZd87yijXWZmZl1REufVRsR84B5eXgJsE8rl29mZlY1PznIzMysBCdOsx4haXtJd0laJOlxSV+qOiazkcg/K2bWO94BvhIRD0raHHhA0tyIeKLqwMxGEh9xmvWIiFgZEQ/m4V+THnHpB4uYdZgTp1kPkjQJ2AtYUGeeHypi1kZOnGY9RtJmwL8C/y0iflU73w8VMWsvJ06zHiLpA6SkeU1E3Fh1PGYjkROnWY/Ivzp0BbAoIv6p6njMRionTrPesT/wGeAgSQ/lvyOqDspspPHXUcx6RETcS/1fHjKzDvIRp5mZWQlOnGZmZiU4cZqZmZXQdOIc6PmZkraUNFfSM/n/uObDNTMzq1Yrjjj7n5/5u8C+wMmSJgOnA3dExC7AHXnczMyspzWdOAd5fuZUYFYuNgs4utm6zMzMqtbSa5w1z8/8UESshJRcgW1aWZeZmVkVWpY4h3p+5iCv8wOpzcysZ7QkcQ7w/MwXJW2b528LvFTvtX4gtZmZ9ZJW3FU70PMzbwGm5+HpwM3N1mVmZla1Vjxyr//5mY9KeihP+ypwHnCDpBnAUuBTLajLzMysUk0nziGen3lws8s3MzPrJn5ykJmZWQlOnGZmZiU4cZqZmZXgxGlmZlaCE6eZmVkJTpxmZmYlOHGamZmV4MRpZmZWghOnmZlZCU6cZmZmJThxmpmZleDEaWZmVoITp5mZWQltTZySDpP0lKTFkk5vZ11mI4H7lFn12pY4JY0C/gU4HJgMHCtpcrvqM1vfuU+ZdYd2HnHuAyyOiCUR8RZwHTC1jfWZre/cp8y6QDsT5wRgWWF8eZ5mVhmpub+KuU+ZdYHRbVx2vbeZWKeQNBOYmUdflfTUIMvcGnh52AG1/42vqfg6oNvjgy6PURoyvh3bWX2daZX2qQ50qq5uD3R/fNDtMUpV9qlhaWfiXA5sXxifCKyoLRQRlwKXNrJASQsjYkprwms9x9e8bo+x4vjcp7pMt8cH3R9jt8dXTztP1f47sIuknSRtCEwDbmljfWbrO/cpsy7QtiPOiHhH0heBHwOjgCsj4vF21We2vnOfMusO7TxVS0TcBtzWwkU2dPqpQo6ved0eY6XxuU91nW6PD7o/xm6Pbx2KWOfeAjMzMxuAH7lnZmZWQlckzqEeIyZpI0nX5/kLJE0qzDsjT39K0qEVxvhlSU9IekTSHZJ2LMx7V9JD+a8tN3M0EN/xkvoKcZxYmDdd0jP5b3pF8Z1fiO1pSWsK8zqx/a6U9JKkxwaYL0kX5fgfkbR3YV7bt19Z7lMdic99avD41qs+tZaIqPSPdJPDs8DOwIbAw8DkmjJfAC7Jw9OA6/Pw5Fx+I2CnvJxRFcX4R8CYPPz5/hjz+KtdsA2PB75d57VbAkvy/3F5eFyn46spfwrpxpeObL9cxwHA3sBjA8w/Arid9F3KfYEFndp+bWoP7lPNx+c+NXiM602fqv3rhiPORh4jNhWYlYfnAAdLUp5+XUS8GRHPAYvz8joeY0TcFRGv59H5pO/YdUozj2I7FJgbEasiYjUwFzis4viOBWa3OIZBRcQ9wKpBikwFro5kPjBW0rZ0ZvuV5T7VgfgG4T7Feten1tINibORx4i9VyYi3gFeAbZq8LWdirFoBumTVL+NJS2UNF/S0RXG91/yKZE5kvq/SN+JbdhwHfl03E7AnYXJ7d5+jRhoHbrxMXjuU52Lz31q+HqpT62lrV9HaVAjjxEbqExDjyBrgYbrkfRpYArw8cLkHSJihaSdgTslPRoRz3Y4vh8CsyPiTUknkY42DmrwtZ2Ir980YE5EvFuY1u7t14iq22AZ7lOdic99qjlVt8Fh64YjzkYeI/ZeGUmjgS1IpwAaegRZh2JE0iHAmcBREfFm//SIWJH/LwHmAXt1Or6I+GUhpsuA/9DoazsRX8E0ak4pdWD7NWKgdehUGyzDfaoD8blPNa2X+tTaqr7ISjrqXUI6ldB/kXv3mjIns/aNDDfk4d1Z+0aGJbTnRoZGYtyLdLF+l5rp44CN8vDWwDMMchG/jfFtWxj+E2B+vH8h/rkc57g8vGWn48vldgWeJ3+/uFPbr1DXJAa+keGTrH0jw/2d2n5tag/uU83H5z41dJzrRZ9aJ/aqA8gb6gjg6dxJzszTvk76lAmwMfAD0o0K9wM7F157Zn7dU8DhFcb4E+BF4KH8d0uevh/waG7YjwIzKorvG8DjOY67gN0Krz0hb9vFwOeqiC+Pnw2cV/O6Tm2/2cBK4G3SJ94ZwEnASXm+SD8i/WyOY0ont18b2oP7VPPxuU8NHt961aeKf35ykJmZWQndcI3TzMysZzhxmpmZleDEaWZmVoITp5mZWQlOnGZmZiU4cZqZmZXgxGlmZlaCE6eZmVkJ/x9fHR0zMya54gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e826c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e785160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286def23c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RhmFmZlbdSBPcYmBOGp4D3Jor/0g6m/JQ4JXBXZlmZmad1MjfBG4E7gX2lbRW0jzgUuBoSSuBo9NrgNuA1cAq4NvAJ9oStdlWSNJfSVoh6VFJN0raodsxmfWyUfVGiIiTa1QdWWXcAM4qGpSZDSVpEvDfgf0i4j8kLQJmk/2Nx8yq8JVMzPrHKGBHSaOAnahxfNvMMk5wZn0gIp4HvgY8BwyQHd++PT+Oz0w2G8oJzqwPpMvhzQKmA3sCO0s6NT+Oz0w2G8oJzqw/HAU8ExEbIuI3wPeAP+pyTGY9zQnOrD88BxwqaSdJIjvJ6/Eux2TW05zgzPpARCwDbgEeBB4h67tXdTUosx5X928CZtYbIuILwBe6HYdZv/AWnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlVKhBFft9h2SpktaJmmlpJslbd+qYM3MzBo14gSXu33HzIg4ANiW7PYdXwYui4gZwMvAvFYEamZm1oyiuygrb98xABxBdsUFgIXASQWXYWZm1rQRJ7hqt+8AHgA2RcTmNNpaYFLRIM3MzJpVZBflFrfvAI6rMmrUmN73rjIzs7Ypsouy1u07xqRdlgCTqXHXYd+7yszM2qlIgqt2+47HgDuBD6Vx5gC3FgvRzMyseUWOwdW6fcd5wNmSVgG7Ade0IE4zM7OmFLpdTo3bd6wGDikyXzMzs6J8JRMzMyslJzgzMyslJzizPiFpjKRbJD0h6XFJ/6XbMZn1skLH4Myso/4G+KeI+FC6xutO3Q7IrJc5wZn1AUlvAT4AzAWIiF8Dv+5mTGa9zrsozfrD3sAG4O8k/bukqyXtnB/BVwcyG8oJzqw/jAIOBr4ZEQcBvwLOz4/gqwOZDeUEZ9Yf1gJr0wUWILvIwsFdjMes5znBmfWBiHgBWCNp31Q0eGk8M6vBJ5mY9Y9PAjekMyhXA6d1OR6znuYEZ9YnIuIhYGa34zDrF95FaWZmpeQEZ2ZmpVQowVW7dJCkcZKWSFqZnse2KlgzM7NGFd2CG7x00DuBPwAeJ/tvztKImAEspeK/OmZmZp0w4gSXu3TQNZBdOigiNgGzgIVptIXASUWDNDMza1aRLbhalw7aIyIGANLz7i2I08zMrClFElzdSwcNx9fNMzOzdiqS4GpdOuhFSRMB0vP6ahP7unlmZtZOI05ww1w6aDEwJ5XNAW4tFKGZmdkIFL2SSbVLB20DLJI0D3gO+HDBZZiZmTWtUIIb5tJBRxaZr5mZWVG+komZmZWSE5yZmZWSE5yZmZWSE5yZmZWSE5yZmZWSE5yZmZWSE5xZn5C0bbru6w+7HYtZP3CCM+sfnyK7JZWZNcAJzqwPSJoMfBC4utuxmPULJziz/nA5cC7w21oj+A4dZkM5wZn1OEknAOsj4oHhxvMdOsyGcoIz633vA06U9CxwE3CEpO90NySz3ucEZ9bjIuKCiJgcEdOA2cAdEXFql8My63mFE1zlqcuSpktaJmmlpJvTrXTMzMw6qhVbcJWnLn8ZuCwiZgAvA/NasAwzAyLirog4odtxmPWDQgmu8tRlSQKOAG5JoywETiqyDDMzs5EougVXeerybsCmiNicXq8FJhVchpmZWdNGnOBqnLqsKqNGjen9nx0zM2ubIltwW5y6TLZFN0bSqDTOZGBdtYn9nx0zM2unESe4GqcunwLcCXwojTYHuLVwlGZmZk1qx//gzgPOlrSK7JjcNW1YhpmZ2bBG1R+lvoi4C7grDa8GDmnFfM3MzEbKVzIxM7NScoIzM7NScoIzM7NScoIzM7NScoIzM7NScoIzM7NScoIzM7NScoIzM7NScoIzM7NScoIz6wOSpki6U9LjklZI+lS3YzLrdS25VJeZtd1m4JyIeFDSrsADkpZExGPdDsysV3kLzqwPRMRARDyYhl8FHsc3EzYblhOcWZ+RNA04CFjW3UjMeluRO3pXPSYgaZykJZJWpuexrQvXbOsmaRfg/wGfjohfVtTNl7Rc0vINGzZ0J0CzHlJkC27wmMC7gEOBsyTtB5wPLI2IGcDS9NrMCpK0HVlyuyEivldZHxFXRcTMiJg5YcKEzgdo1mOK3NG71jGBWcDCNNpC4KSiQZpt7SSJ7ObBj0fEX3c7HrN+0JJjcBXHBPaIiAHIkiCweyuWYbaVex/wF8ARkh5Kj+O7HZRZLyv8N4HKYwLZD82GppsPzAeYOnVq0TDMSi0ifgo01rnMDCi4BVfjmMCLkiam+onA+mrT+niBmZm1U5GzKGsdE1gMzEnDc4BbRx6emZnZyBTZRTl4TOARSQ+lss8ClwKLJM0DngM+XCxEMzOz5o04wdU5JnDkSOdrZmbWCr6SiZmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlZITnJmZlVLbEpykYyU9KWmVpPPbtRyzrYX7lFlz2pLgJG0L/C1wHLAfcLKk/dqxLLOtgfuUWfPatQV3CLAqIlZHxK+Bm4BZbVqW2dbAfcqsSe1KcJOANbnXa1OZWddIxR5d5j5l1qQR39G7jmqrgxgygjQfmJ9evibpyWHmNx54acTBdGblVCjGDnB8BUl1Y9yrnYuvUta1PtUhvR5jsfjav2Lq9faD+jEW6lPtSnBrgSm515OBdfkRIuIq4KpGZiZpeUTMbF14rdfrMTq+4roco/tUj3F8xbU7xnbtovw3YIak6ZK2B2YDi9u0LLOtgfuUWZPasgUXEZsl/SXwz8C2wLURsaIdyzLbGrhPmTWvXbsoiYjbgNtaNLuGdrt0Wa/H6PiK62qM7lM9x/EV19YYFRH1xzIzM+szvlSXmZmVUtcTXL3LD0kaLenmVL9M0rRc3QWp/ElJx3QpvrMlPSbpYUlLJe2Vq3tT0kPp0bYTAhqIca6kDblYPpqrmyNpZXrM6VJ8l+Vie0rSplxd29tQ0rWS1kt6tEa9JH09xf+wpINzdW1vv5GSNE7SkhTbEklja4xXtY3TCS3L0vQ3p5NbOhqfpAMl3StpRWr7P8/VLZD0TC72A1sYm9dL7Y2vM+ukiOjag+xg+dPA3sD2wM+A/SrG+QRwZRqeDdychvdL448Gpqf5bNuF+P4Y2CkNnzkYX3r9Wo+04VzgG1WmHQesTs9j0/DYTsdXMf4nyU6g6GQbfgA4GHi0Rv3xwI/I/ot2KLCsU+1X8H19BTg/DZ8PfLnGeFXbGFgEzE7DVwJndjo+4B3AjDS8JzAAjEmvFwAfakO7eb3U/vg6sk7q9hZcI5cfmgUsTMO3AEdKUiq/KSLeiIhngFVpfh2NLyLujIjX08v7yP6f1ElFLuF0DLAkIjZGxMvAEuDYLsd3MnBji2MYVkTcA2wcZpRZwHWRuQ8YI2kinWm/IvJ9ZyFwUqMTpj52BFmfa3r6BtWNLyKeioiVaXgdsB6Y0OI4Knm91Ob4htHSPtXtBNfI5Yd+N05EbAZeAXZrcNpOxJc3j+yX/qAdJC2XdJ+kVq8cBjUa45+l3RW3SBr8w3BPtWHajTIduCNX3Ik2rKfWe+j1y2ftEREDAOl59xrjVWvj3YBNqc9Be95bo/EBIOkQsi2Cp3PFF6fv9WWSRrcoLq+XOhNf29dJbfubQIPqXn5omHEambaohpch6VRgJnBYrnhqRKyTtDdwh6RHIuLpatO3OcYfADdGxBuSziD75XlEg9N2Ir5Bs4FbIuLNXFkn2rCebn4HhyXpx8DbqlRd2MRstmhj4JdVxmv6vbUoPtIW8/XAnIj4bSq+AHiBLOldBZwHXNRsjNUWV6XM66XWxteRdVK3t+DqXn4oP46kUcBbyXYnNTJtJ+JD0lFkHfbEiHhjsDztUiEiVgN3AQe1OL6GYoyIX+Ti+jbwnkan7UR8ObOp2D3ZoTasp9Z76ET7DSsijoqIA6o8bgVeTIlhMEGsrzGPam38Etmu2MEfwSN6b62IT9JbgH8EPpd2EQ/OeyDtNn4D+DtatyvQ66U2x9exddJID9614kG2BbmabLfU4MHI/SvGOYuhB3MXpeH9GXowdzWtP5jbSHwHke0ymVFRPhYYnYbHAysZ5uSKNsc4MTf8p8B98fsDus+kWMem4XGdji+Nty/wLOm/mZ1swzT/adQ+yeSDDD3J5P5OtV/B9/RVhp7E8ZUq49RsY+C7DD3J5BNdiG97YCnw6Sp1E9OzgMuBSzv1nfV6qXB8HVkndbTD1WiM44Gn0odxYSq7iOxXB8AOqaOtAu4H9s5Ne2Ga7knguC7F92PgReCh9Ficyv8IeCR9uI8A87rYhpcAK1IsdwLvzE17emrbVcBp3Ygvvf5i5QqqU21IttU4APyG7BfkPOAM4IxUL7KbjT6d4pjZyfYr8L52I0sOK9PzuFQ+E7i6XhuTnQV3f3pv3yWtGDsc36npc3ko9zgw1d2RYn4U+A6wS6e+s3i9VDS+jqyTfCUTMzMrpW4fgzMzM2sLJzgzMyslJzgzMyslJzgzMyslJzgzMyslJzgzMyslJzgzMyslJzgzMyul/w/PCHK0ylOj2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286dfa8908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2287221f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df95ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df6b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286febac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0fBRwRUQ8FxEPAEuBfTsZj5mZWa+q+xzwCcB1aXgysLxUtiKNM7MhtDjdM0HSQklL0t/xOWM0s8HVloAlnQG8AFzeGFVRLVpMO0fSIkmL1qxZ060QzUaTuWx4uuc04PqImAZcn96bWY+qJQFLmgUcARwbEY0kuwKYUqq2C7CyavqIuCgiZkTEjIkTJ3Y3WLNRoOp0D8VpnXlpeB5wdK1BmdmwdD0BSzoUOBU4MiKeLRVdA8yUtIWk3YFpwC+7HY9ZH9spIlYBpL87tqroo0pm+XX6Z0jzgV8Ae0haIWk2cD6wHbBQ0mJJFwJExF3AAuC3wA+AkyLixU7GY2bVfFTJLL8xnZxZRBxTMfriQeqfCZzZyRjMNmGPSpoUEaskTQJW5w7IzFrznbDM+sc1wKw0PAu4OmMsZjYEJ2CzUajF6Z6zgIMlLQEOTu/NrEd19BC0mdWjxekegANrDcTMNpr3gM3MzDJwAjYzM8vACdjMzCwDJ2AzM7MMnIDNzMwycAI2MzPLwAnYzMwsAydgMzOzDJyAzczMMnACNjMzy6DTjyO8RNJqSXeWxk2QtFDSkvR3fBovSV+VtFTS7ZL26WQsZmZmvazTe8BzgUObxp0GXB8R04Dr03uAw4Bp6TUHuKDDsZiZmfWsjibgiPgpsLZp9FHAvDQ8Dzi6NP7SKNwMjEvPMDUzM+t7dZwD3ikiVgGkvzum8ZOB5aV6K9I4MzOzvpfzIixVjIvKitIcSYskLVqzZk2XwzIzM+u+OhLwo41Dy+nv6jR+BTClVG8XYGXVDCLiooiYEREzJk6c2NVgzczM6lBHAr4GmJWGZwFXl8Z/IF0NvR/wRONQtZmZWb/r9M+Q5gO/APaQtELSbOAs4GBJS4CD03uAa4FlwFLgG8BHOhmL2aZK0t9LukvSnZLmS9oyd0xmtqExnZxZRBzToujAiroBnNTJ5Ztt6iRNBj4GTI+IP0haAMyk+ImgmfUQ3wnLrP+MAbaSNAbYmhbXVphZXk7AZn0kIh4Gvgw8BKyiuLbiR831/MsCs/ycgM36SLrV61HA7sDOwDaSjmuu518WmOXnBGzWXw4CHoiINRHxJ+DfgTdnjsnMKjgBm/WXh4D9JG0tSRQXQN6dOSYzq+AEbNZHIuIW4Crg18AdFH38oqxBmVmljv4Myczyi4jPAJ/JHYeZDc57wGZmZhk4AZuZmWXgBGxmZpaBE7CZmVkGTsBmZmYZOAGbmZllUFsCrnpEmqTdJd0iaYmkKyWNrSseMzOznGpJwKVHpM2IiL2AzSkekfZF4JyImAY8DsyuIx4zM7Pc6jwE3fyItFXAARR37QGYBxxdYzxmZmbZ1JKAqx6RBtwKrIuIF1K1FcDkOuIxMzPLra5D0Bs8Ig04rKJqtJjezy41M7O+Utch6FaPSBuXDkkD7AKsrJrYzy41M7N+U1cCrnpE2m+BnwDvTXVmAVfXFI+ZmVlWdZ0DbvWItFOBT0paCmwPXFxHPGZmZrnV9jjCFo9IWwbsW1cMZmZmvcJ3wjIzM8vACdjMzCwDJ2CzPiNpnKSrJN0j6W5J/zV3TGa2odrOAZtZbc4DfhAR7033V986d0BmtiEnYLM+IumVwNuB4wEi4nng+ZwxmVk1H4I26y+vAdYA35L0G0nflLRNcyXfXc4sPydgs/4yBtgHuCAi3gQ8A5zWXMl3lzPLzwnYrL+sAFakm99AcQOcfTLGY2YtOAGb9ZGIeARYLmmPNKpx21cz6zG+CMus/3wUuDxdAb0M+GDmeMysghOwWZ+JiMXAjNxxmNngfAjazMwsAydgMzOzDGpLwFW3x5M0QdJCSUvS3/F1xWNmZpZTnXvAjdvj/TnwBuBuit8nXh8R04Drqfi9opmZWT+qJQGXbo93MRS3x4uIdcBRwLxUbR5wdB3xmJmZ5VbXHnCr2+PtFBGrANLfHWuKx8zMLKu6EnBbt8drxfetNTOzflNXAm51e7xHJU0CSH9XV03s+9aamVm/qSUBD3J7vGuAWWncLODqOuIxMzPLrc47YVXdHm8zYIGk2cBDwPtqjMfMzCyb2hLwILfHO7CuGMzMzHqF74RlZmaWgROwmZlZBk7AZmZmGTgBm5mZZeAEbGZmloETsJmZWQZOwGZ9SNLm6b7r/5E7FjOr5gRs1p8+TvHITzPrUU7AZn1G0i7Au4Bv5o7FzFpzAjbrP+cCpwAvtargJ4yZ5ecEbNZHJB0BrI6IWwer5yeMmeXnBGzWX94CHCnpQeAK4ABJl+UNycyqOAGb9ZGIOD0idomIqcBM4IaIOC5zWGZWodYE3PzTCEm7S7pF0hJJV6ZHFZqZmfW9uveAm38a8UXgnIiYBjwOzK45HrO+FRE3RsQRueMws2q1JeDmn0ZIEnAAcFWqMg84uq54zMzMcqpzD7j5pxHbA+si4oX0fgUwucZ4zMzMsqklAbf4aYQqqkaL6f2bRTMz6yt17QFv8NMIij3icZLGpDq7ACurJvZvFs3MrN/UkoBb/DTiWOAnwHtTtVnA1XXEY2Zmllvu3wGfCnxS0lKKc8IXZ47HzMysFmOGrtJZEXEjcGMaXgbsW3cMZmZmueXeAzYzM9skOQGbmZll4ARsZmaWgROwmZlZBk7AZmZmGTgBm5mZZeAEbGZmloETsJmZWQZOwGZmZhk4AZv1EUlTJP1E0t2S7pL08dwxmVm12m9FaWZd9QJwckT8WtJ2wK2SFkbEb3MHZmYDeQ/YrI9ExKqI+HUafgq4G5icNyozq+IEbNanJE0F3gTckjcSM6tSSwJudV5K0gRJCyUtSX/H1xGPWb+TtC3wf4BPRMSTFeVzJC2StGjNmjX1B2hmte0BN85LvR7YDzhJ0nTgNOD6iJgGXJ/em9kISHoFRfK9PCL+vapORFwUETMiYsbEiRPrDdDMgJoS8CDnpY4C5qVq84Cj64jHrF9JEnAxcHdEfCV3PGbWWu3ngJvOS+0UEaugSNLAjnXHY9Zn3gK8HzhA0uL0Ojx3UGa2oVp/htR8Xqr4Z72t6eYAcwB23XXX7gVoNspFxM+A9jqWmWVV2x5wi/NSj0qalMonAaurpvX5KjMz6zd1XQXd6rzUNcCsNDwLuLqOeMzMzHKr6xB047zUHZIWp3GfAs4CFkiaDTwEvK+meMzMzLKqJQEPcV7qwDpiMDMz6yW+E5aZmVkGTsBmZmYZOAGbmZll4ARsZmaWgROwmZlZBk7AZmZmGTgBm5mZZeAEbGZmloETsJmZWQZOwGZmZhk4AZuZmWXgBGxmZpaBE7CZmVkG2ROwpEMl3StpqaTTcsdjNtq5T5mNDlkTsKTNga8BhwHTgWMkTc8Zk9lo5j5lNnrk3gPeF1gaEcsi4nngCuCozDGZjWbuU2ajRO4EPBlYXnq/Io0zy0Ya2Ssz9ymzUWJM5uVXba5ig0rSHGBOevu0pHsHmecOwGMbHVD3N6Ajiq8GvR4f9HiM0pDx7dbNxVeMy9qnatDr8cFIY+z+hqnX2zBnn+qa3Al4BTCl9H4XYGVzpYi4CLionRlKWhQRMzoTXuc5vpHr9Rgzx+c+1YN6PUbHl0fuQ9C/AqZJ2l3SWGAmcE3mmMxGM/cps1Ei6x5wRLwg6e+AHwKbA5dExF05YzIbzdynzEaP3IegiYhrgWs7OMu2Dqtl5PhGrtdjzBqf+1RP6vUYHV8Gitjg+gwzMzPrstzngM3MzDZJoyYBD3V7PUlbSLoyld8iaWqp7PQ0/l5Jh2SM8ZOSfivpdknXS9qtVPaipMXp1ZWLZtqI73hJa0pxfKhUNkvSkvSalSm+c0qx3SdpXamsjva7RNJqSXe2KJekr6b4b5e0T6ms6+03EpImSFqY4lsoaXyLepXtnC76uiVNf2W6AKzW+CS9UdIvJN2V2v+vS2VzJT1Qiv2NHYqrp7dLvb5NajPGrNulroqInn9RXExyP/AaYCxwGzC9qc5HgAvT8EzgyjQ8PdXfAtg9zWfzTDG+E9g6DX+4EWN6/3QPtOHxwPkV004AlqW/49Pw+Lrja6r/UYoLjGppv7SMtwP7AHe2KD8cuI7it7j7AbfU1X4d+GxnA6el4dOAL7aoV9nOwAJgZhq+EPhw3fEBrwOmpeGdgVXAuPR+LvDeDsfU09ulXt8mDSPGbNulbr9Gyx5wO7fXOwqYl4avAg6UpDT+ioh4LiIeAJam+dUeY0T8JCKeTW9vpviNZl1GcovCQ4CFEbE2Ih4HFgKHZo7vGGB+h2MYVET8FFg7SJWjgEujcDMwTtIk6mm/kSr3n3nA0e1OmPrZART9btjTt2nI+CLivohYkoZXAquBiR2Oo6zXt0u9vk1qK8ZBjIZ+NajRkoDbub3e+joR8QLwBLB9m9PWFWPZbIq9pYYtJS2SdLOkTm+8hhPfX6XDUVdJatzQoY42bHsZ6TDZ7sANpdHdbr92tPoMo+H2kDtFxCqA9HfHFvWq2nl7YF3qd9Cdz9dufABI2pdij+r+0ugz07p9jqQtOhBTr2+Xen2bBL2/Xeqq7D9DalM7t9drVaetW/N1QNvLkXQcMAN4R2n0rhGxUtJrgBsk3RER91dN38X4vgfMj4jnJJ1I8Z/7AW1OW0d8DTOBqyLixdK4brdfO3Kvg4OS9GPg1RVFZwxjNhu0M/BkRb1hf74OxUc66vBtYFZEvJRGnw48QpGULwJOBT433BibF1Uxrpe2S72+TWo3xpzbpa4aLXvA7dxeb30dSWOAV1EcLmzr1nw1xYikgyg2KEdGxHON8emQGRGxDLgReFPd8UXE70sxfQP4i3anrSO+kpk0HX6uof3a0eoz1LUODioiDoqIvSpeVwOPpsTVSGCrW8yjqp0fozjc3viHfqM+Xyfik/RK4PvAp9NpgMa8V6VTA88B36Izh3t7fbvU69uktmLMvF3qrtwnodt5UeypL6M47Ng4Ub9nU52TGHixw4I0vCcDL3ZYRncuwmonxjdRHBKb1jR+PLBFGt4BWMIgFyB1Mb5JpeH3ADen4QnAAynO8Wl4Qt3xpXp7AA+SfsNeV/uVljWV1hdhvYuBF2H9sq7268Dn+hIDL3I6u6JOy3YGvsPAi7A+kiG+scD1wCcqyialvwLOBc6qY53NuV3q9W3SMGLMtl3q9it7AMP4og4H7ksryxlp3Oco/msD2DJtBJYCvwReU5r2jDTdvcBhGWP8MfAosDi9rknj3wzckVa+O4DZmeL7AnBXiuMnwJ+Xpj0hte1S4IM54kvvP9u88ayx/eZTXFn7J4r/vmcDJwInpnIBX0vx3wHMqLP9RvjZtqdIXkvS3wlp/Azgm0O1M8VVrL9Mn+87pI13zfEdl76bxaXXG1PZDSnmO4HLgG3rWGfJvF1qI76s26Q2Y8y6Xermy3fCMjMzy2C0nAM2MzPrK07AZmZmGTgBm5mZZeAEbGZmloETsJmZWQZOwGZmZhk4AZuZmWXgBGxmZpbB/weVhu3wUPx2ZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ffe9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e7f2eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286fec2588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22871ff95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22872030cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228720c62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228722f12b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e76a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286e20fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22872048d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df83128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ded4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df74518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286df102e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bgJm5eyZwYxdjMRuWnAzNekiN0xIXAIdLWg4cnvvNrIXqNpOaWefUOC0BcGhHAzErGR8ZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TkZmplZ6TXyCKcrJK2T9HBh2FhJCyQtz+9j8nBJ+pqkFZIekrR/O4M3MzNrhUaODK8EjqwYNhdYGBFTgYW5H+Ao0gN9pwJzgG+2JkwzM7P2qZsMI+JOYEPF4OnAvNw9DziuMPyqSO4BRufnr5mZmfWswZ4z3Dki1gLk953y8AnAqkK51XmYmZlZz2r1BTSqMiyqFpTmSFokadH69etbHIaZmVnjBpsMn+1r/szv6/Lw1cBuhXITgTXVZhARl0XEtIiYNm7cuEGGYWZm1rzBJsObgJm5eyZwY2H4R/NVpQcCL/U1p5qZmfWqRv5acQ1wN7CnpNWSZgMXAIdLWg4cnvsBbgZWAiuAbwGfbEvUZiUk6f9KWirpYUnXSNq62zGZDRcj6hWIiONrjDq0StkATm02KDPblKQJwP8B9oqI/5A0H5hB+uuTmTXJd6AxGzpGANtIGgGMosb5eDMbOCdDsyEgIn4NfBl4GlhLOh9/S7GMr9A2GzwnQ7MhIN/ycDqwO7ArsK2kE4tlfIW22eA5GZoNDYcBT0bE+oj4HfBD4KAux2Q2bDgZmg0NTwMHSholSaQL2JZ1OSazYcPJ0GwIiIh7geuBB4AlpLp7WVeDMhtG6v61wsx6Q0ScDZzd7TjMhiMfGZqZWek5GZqZWek5GZqZWek5GZqZWek5GZqZWek5GZqZWek1lQyrPVJG0u6S7pW0XNJ1krZqVbBmZmbtMOhkWHikzLSI2AfYkvRImQuBiyNiKvACMLsVgZqZmbVLs82klY+UWQscQrpTBsA84Lgml2FmZtZWg06G1R4pA9wPvBgRG3Ox1cCEZoM0MzNrp2aaSTd7pAxwVJWiUWN6P3vNzMx6QjPNpLUeKTM6N5sCTKTG07j97DUzM+sVzSTDao+UeQS4DfhQLjMTuLG5EM3MzNqrmXOGtR4pczrwGUkrgB2Ay1sQp5mZWds09QinGo+UWQkc0Mx8zczMOsl3oDEzs9JzMjQzs9JzMjQbIiSNlnS9pEclLZP037odk9lw0dQ5QzPrqK8CP4mID+V7/o7qdkBmw4WTodkQIOmPgPcDswAi4nXg9W7GZDacuJnUbGjYA1gP/LOkf5f0bUnbFgv4rk5mg+dkaDY0jAD2B74ZEfsBrwBziwV8VyezwXMyNBsaVgOr880uIN3wYv8uxmM2rDgZmg0BEfEMsErSnnlQ3+0PzawFfAGN2dBxGvDdfCXpSuBjXY7HbNhwMjQbIiJiMTCt23GYDUduJjUzs9JzMjQzs9JrKhlWuz2UpLGSFkhant/HtCpYMzOzdmj2yLDv9lDvBP4YWEb679PCiJgKLKTiv1BmZma9ZtDJsHB7qMsh3R4qIl4EpgPzcrF5wHHNBmlmZtZOzRwZ1ro91M4RsRYgv+/UgjjNzMzapplkWPf2UP3xfRTNzKxXNJMMa90e6llJ4wHy+7pqE/s+imZm1isGnQz7uT3UTcDMPGwmcGNTEZqZmbVZs3egqXZ7qC2A+ZJmA08DH25yGWZmZm3VVDLs5/ZQhzYzXzMzs07yHWjMzKz0nAzNzKz0nAzNzKz0nAzNzKz0nAzNzKz0nAzNzKz0nAzNhghJW+b7AP+o27GYDTdOhmZDx1+SHpNmZi3mZGg2BEiaCHwQ+Ha3YzEbjpwMzYaGS4DPAb+vVcBPgjEbPCdDsx4n6RhgXUTc3185PwnGbPCcDM1633uBYyU9BVwLHCLp6u6GZDa8OBma9biIOCMiJkbEFGAGcGtEnNjlsMyGlaaTYeXl3pJ2l3SvpOWSrsuPdzIzM+tZrTgyrLzc+0Lg4oiYCrwAzG7BMswMiIjbI+KYbsdhNtw0lQwrL/eWJOAQ4PpcZB5wXDPLMDMza7dmjwwrL/feAXgxIjbm/tXAhCaXYWZm1laDToY1LvdWlaJRY3r/J8rMzHpCM0eGm13uTTpSHC1pRC4zEVhTbWL/J8rMzHrFoJNhjcu9TwBuAz6Ui80Ebmw6SjMzszZqx/8MTwc+I2kF6Rzi5W1YhpmZWcuMqF+kvoi4Hbg9d68EDmjFfM3MzDrBd6AxM7PSczI0M7PSczI0M7PSczI0M7PSczI0M7PSczI0M7PSczI0M7PSczI0M7PSczI0M7PSczI0GwIk7SbpNknLJC2V9JfdjslsOGnJ7djMrO02Ap+NiAckbQ/cL2lBRDzS7cDMhgMfGZoNARGxNiIeyN2/BZbhB2ebtYyTodkQI2kKsB9wb3cjMRs+mnnSfdVzGJLGSlogaXl+H9O6cM3KTdJ2wA+AT0fEbyrGzZG0SNKi9evXdydAsyGqmSPDvnMY7wIOBE6VtBcwF1gYEVOBhbnfzJok6S2kRPjdiPhh5fiIuCwipkXEtHHjxnU+QLMhrJkn3dc6hzEdmJeLzQOOazZIs7KTJNKDspdFxFe6HY/ZcNOSc4YV5zB2joi1kBImsFMrlmFWcu8FPgIcImlxfh3d7aDMhoum/1pReQ4j/YBtaLo5wByASZMmNRuG2bAWET8HGqtcZjZgTR0Z1jiH8ayk8Xn8eGBdtWl9fsPMzHpFM1eT1jqHcRMwM3fPBG4cfHhmZmbt10wzad85jCWSFudhnwcuAOZLmg08DXy4uRDNzMzaa9DJsM45jEMHO18zM7NO8x1ozMys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9JwMzcys9NqWDCUdKekxSSskzW3XcszKwnXKrH3akgwlbQn8I3AUsBdwvKS92rEsszJwnTJrr3YdGR4ArIiIlRHxOnAtML1NyzIrA9cpszZqVzKcAKwq9K/Ow8y6Rmru1WWuU2ZtNOgn3ddRbdcRmxSQ5gBzcu/Lkh7rZ347As8NOpjO7MiairEDHF+TpLoxTm7n4qsM61qd6pBej7G5+Nq/Y+r19Qf1Y2xnndpEu5LhamC3Qv9EYE2xQERcBlzWyMwkLYqIaa0Lr/V6PUbH17wux+g61WMcX/N6KcZ2NZP+EpgqaXdJWwEzgJvatCyzMnCdMmujthwZRsRGSZ8CfgpsCVwREUvbsSyzMnCdMmuvdjWTEhE3Aze3aHYNNf10Wa/H6Pia19UYXad6juNrXs/EqIioX8rMzGwY8+3YzMys9LqeDOvdYkrSSEnX5fH3SppSGHdGHv6YpCO6FN9nJD0i6SFJCyVNLox7Q9Li/GrbxQ4NxDhL0vpCLB8vjJspaXl+zexSfBcXYntc0ouFcW1fh5KukLRO0sM1xkvS13L8D0navzCu7etvsCSNlbQgx7ZA0pga5aqu43yxzr15+uvyhTsdjU/SvpLulrQ0r/s/L4y7UtKThdj3bWFs3i+1N76u7pOqioiuvUgXAjwB7AFsBTwI7FVR5pPApbl7BnBd7t4rlx8J7J7ns2UX4vsfwKjcfUpffLn/5R5Zh7OAr1eZdiywMr+Pyd1jOh1fRfnTSBeHdHIdvh/YH3i4xvijgR+T/ut3IHBvp9Zfk5/rImBu7p4LXFijXNV1DMwHZuTuS4FTOh0f8A5gau7eFVgLjM79VwIfasN6836p/fF1bZ9U69XtI8NGbjE1HZiXu68HDpWkPPzaiHgtIp4EVuT5dTS+iLgtIl7NvfeQ/v/VSc3cpusIYEFEbIiIF4AFwJFdju944JoWx9CviLgT2NBPkenAVZHcA4yWNJ7OrL9mFOvOPOC4RifMdewQUp0b8PQNqhtfRDweEctz9xpgHTCuxXFU8n6pzfH1o2t1qtvJsJFbTL1ZJiI2Ai8BOzQ4bSfiK5pNOoLos7WkRZLukdTqHUmfRmP8s9xkcr2kvj9v99Q6zE05uwO3FgZ3Yh3WU+sz9Pot0naOiLUA+X2nGuWqreMdgBdznYP2fLZG4wNA0gGkI40nCoPPz9v1xZJGtigu75c6E1+39klVte2vFQ2qe4upfso0Mm2zGl6GpBOBacAHCoMnRcQaSXsAt0paEhFPVJu+zTH+K3BNRLwm6WTSL9pDGpy2E/H1mQFcHxFvFIZ1Yh3W081tsF+SfgbsUmXUmQOYzWbrGPhNlXID/mwtio98JP4dYGZE/D4PPgN4hpQgLwNOB84daIzVFldlmPdLrY2vm/ukqrp9ZFj3FlPFMpJGAG8lNWk1Mm0n4kPSYaTKfWxEvNY3PDfrEBErgduB/VocX0MxRsTzhbi+BfzXRqftRHwFM6hoIu3QOqyn1mfoxPrrV0QcFhH7VHndCDybk0hfMllXYx7V1vFzpObgvh/Mg/psrYhP0h8B/waclZup++a9Njddvwb8M61rjvR+qc3xdXmfVF0nTkzWepGOTFeSmsb6TrTuXVHmVDY9UT0/d+/NpieqV9L6E9WNxLcfqdlmasXwMcDI3L0jsJx+Lhxpc4zjC93/C7gn/nCy+skc65jcPbbT8eVyewJPkf/72sl1mOc/hdoX0HyQTS+gua9T66/Jz/QlNr1A5aIqZWquY+D7bHoBzSe7EN9WwELg01XGjc/vAi4BLujUNuv9UtPxdW2fVDPuTiykzoo7Gng8f3Fn5mHnkn7NAGydK+UK4D5gj8K0Z+bpHgOO6lJ8PwOeBRbn1015+EHAkrwhLAFmd3EdfhFYmmO5DXhnYdqT8rpdAXysG/Hl/nMqd2adWoeko9G1wO9Iv0xnAycDJ+fxIj1Y94kcx7ROrr8mPtcOpESyPL+PzcOnAd+ut45JVwPelz/b98k70Q7Hd2L+XhYXXvvmcbfmmB8Grga269Q2i/dLzcbX1X1StZfvQGNmZqXX7XOGZmZmXedkaGZmpedkaGZmpedkaGZmpedkaGZmpedkaGZmpedkaGZmpedkaGZmpff/AcTaJgqfaVZlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ffca9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228721b7f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286ffe1860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xv70BmJaHZwOXRMTzEbGK9HDuA9qM18zMrG9UfU30fcDP8/BUYHVh2po8zszMbItQWRKVdCawEfjRwKg6s0WDZd27ipmZDTuVJFFJc4FjgHdFxECiXAPsUphtGrC23vLuXcXMzIajtpOopCOB04B3RMRzhUmLgDmSxkiaAcwEbmy3PDMzs37RUmcLuQeVQ4AdJa0BziLdjTsGWCwJ4IaIODki7pR0GXAX6TTvKRHxUpXBm5mZ9VJLSbRBDyrfG2T+c4BzWg3KzMxsOHCPRWZmZiU5iZqZmZXkJGpmZlaSk6iZmVlJTqJmZmYlOYmamZmV5CRqZmZWkpOomZlZSU6iZmZmJTmJmg1Dkj4h6U5Jd0haIGnrXsdkNhI5iZoNM5KmAh8FZkXEPsAoYE5vozIbmZxEzYan0cBYSaOBcTR4zKCZdZaTqNkwExG/A74KPAisA56MiF/0NiqzkamlJCrpQknrJd1RGDdR0mJJy/PrhDxekr4haYWk2yTtX3XwZiNRbmOzgRnAFGAbSe+umeckScskLduwYUMvwjQbEVo9Er0IOLJm3OnAkoiYCSzJ7wGOIj2IeyZwEvDt8mGaWcHhwKqI2BARLwI/BQ4qzhARF0TErIiYNWnSpJ4EaTYStJREI+Ja4LGa0bOB+Xl4PnBsYfzFkdwAjJc0uZ1gzQxIp3EPlDROkoDDgLt7HJPZiFTFNdGdI2IdQH7dKY+fCqwuzLcmj9uMTz2ZNS8ilgILgZuB20nt+IKeBmU2QnXyxiLVGRf1ZvSpJ7PWRMRZEbFnROwTEe+JiOd7HZPZSFRFEn144DRtfl2fx68BdinMNw3fhm9mZluQKpLoImBuHp4LXF4Y/958l+6BpNvw11VQnpmZWV8Y3crMkhYAhwA7SloDnAWcC1wmaR7phofj8uxXAEcDK4DngBMritnMzKwvtJREI+L4BpMOqzNvAKeUCcrMzGw4cI9FZmZmJTmJmpmZleQkamZmVpKTqJmZWUlOomZmZiU5iZqZmZXkJGpmZlaSk6iZmVlJTqJmZmYlOYmamZmV5CRqZmZWkpOomZlZSU6iZmZmJVWWRCV9QtKdku6QtEDS1pJmSFoqabmkSyVtVVV5ZmZmvVZJEpU0FfgoMCsi9gFGAXOALwPnRcRM4HFgXhXlmZmZ9YMqT+eOBsZKGg2MA9YBhwIL8/T5wLEVlmdmZtZTlSTRiPgd8FXgQVLyfBK4CXgiIjbm2dYAU+stL+kkScskLduwYUMVIZlt0SSNl7RQ0m8l3S3pzb2OyWwkqup07gRgNjADmAJsAxxVZ9aot3xEXBARsyJi1qRJk6oIyWxL93XgXyJiT+BNwN09jsdsRBpd0XoOB1ZFxAYAST8FDgLGSxqdj0anAWsrKs9sxJK0PfBW4ASAiHgBeKGXMZmNVFVdE30QOFDSOEkCDgPuAq4C3pnnmQtcXlF5ZiPZ7sAG4PuS/l3SdyVtU5zBl0jMuqOqa6JLSTcQ3Qzcntd7AXAa8ElJK4AdgO9VUZ7ZCDca2B/4dkTsBzwLnF6cwZdIzLqjqtO5RMRZwFk1o1cCB1RVhpkB6Sa9NfnHK6QfsKcPMr+ZdYh7LDIbZiLiIWC1pD3yqIHLJ2bWZZUdiZpZV50K/Cj3ArYSOLHH8ZiNSE6iZsNQRNwCzOp1HGYjnU/nmpmZleQkamZmVpKTqJmZWUlOomZmZiU5iZqZmZXkJGpmZlaSk6iZmVlJTqJmZmYlOYmamZmV5CRqZmZWUmVJVNJ4SQsl/VbS3ZLeLGmipMWSlufXCVWVZ2Zm1mtVHol+HfiXiNgTeBNwN+nxTEsiYiawBD+uyczMtiCVJFFJ2wNvJT90OyJeiIgngNnA/DzbfODYKsozMzPrB1Udie4ObAC+L+nfJX1X0jbAzhGxDiC/7lRvYUknSVomadmGDRsqCsnMzKyzqkqio4H9gW9HxH7As7Rw6jYiLoiIWRExa9KkSRWFZGZm1llVJdE1wJqIWJrfLyQl1YclTQbIr+srKs/MzKznKkmiEfEQsFrSHnnUYcBdwCJgbh43F7i8ivLMzMz6wegK13Uq8CNJWwErgRNJSfoySfOAB4HjKizPzMyspypLohFxCzCrzqTDqirDzMysn7jHIrNhSNKofCf8P/U6FrORzEnUbHj6GKlDEzPrISdRs2FG0jTg7cB3ex2L2UjnJGo2/HwN+AzwH41mcAcmZt3hJGo2jEg6BlgfETcNNp87MDHrDidRs+HlLcA7JN0PXAIcKumHvQ3JbORyEjUbRiLijIiYFhHTgTnAlRHx7h6HZTZiOYmamZmVVGWPRWbWRRFxNXB1j8MwG9F8JGpmZlaSk6iZmVlJTqJmZmYlOYmamZmVVGkSre0UW9IMSUslLZd0aX5MmpmZ2Rah6iPR2k6xvwycFxEzgceBeRWXZ2Zm1jOVJdHaTrElCTgUWJhnmQ8cW1V5ZmZmvVblkWhtp9g7AE9ExMb8fg0wtd6C7izbzMyGo0qSaINOsVVn1qi3vDvLNjOz4aiqHosGOsU+Gtga2J50ZDpe0uh8NDoNWFtReWZmZj1XyZFog06x3wVcBbwzzzYXuLyK8szMzPpBp/9P9DTgk5JWkK6Rfq/D5ZmZmXVN5R3QFzvFjoiVwAFVl2FmZtYP3GORmZlZSU6iZmZmJTmJmpmZleQkamZmVpKTqJmZWUlOomZmZiU5iZoNM5J2kXSVpLsl3SnpY72OyWykqvz/RM2s4zYCn4qImyVtB9wkaXFE3NXrwMxGGh+Jmg0zEbEuIm7Ow0+TnuFb9wlJZtZZTqJmw5ik6cB+wNLeRmI2MjmJmg1TkrYF/gH4eEQ8VTPNz+g16wInUbNhSNKrSQn0RxHx09rpfkavWXdU9VDuuncLSpooabGk5fl1QhXlmY1kkkR6ItLdEfE3vY7HbCSr6kh04G7BPwIOBE6RtBdwOrAkImYCS/J7M2vPW4D3AIdKuiX/Hd3roMxGokr+xSUi1gHr8vDTkgbuFpwNHJJnm096RNppVZRpNlJFxHWAeh2HmXXgmmjN3YI75wQ7kGh3qro8MzOzXqk0iQ52t+AQy/lOQjMzG3YqS6IN7hZ8WNLkPH0ysL7esr6T0MzMhqOq7s5tdLfgImBuHp4LXF5FeWZmZv2gqr5zB+4WvF3SLXncZ4FzgcskzQMeBI6rqDwzM7Oeq+ru3MHuFjysijLMzMz6jXssMjMzK8lJ1MzMrCQnUTMzs5L8UG4zM2uf2uxEK6KaOLrMR6JmZmYlOYmamZmV5CRqZmZWkpOomZlZSU6iZmZmJTmJmpmZleQkamZmVpKTqJmZWUlOomZmZiV1PIlKOlLSPZJWSDq90+WZjQRuV2b9oaNJVNIo4JvAUcBewPGS9upkmWZbOrcrs/7R6SPRA4AVEbEyIl4ALgFmd7hMsy2d25VZn+h0Ep0KrC68X5PHmVl5bldmfaLTT3Gp163/Zl31SzoJOCm/fUbSPYOsc0fgkQpi66R+j7Gt+Np9WEMT+n37wdAx7tbBsodsV11tU12oEPR/nXB87ZJ62aZK63QSXQPsUng/DVhbO1NEXABc0MwKJS2LiFnVhNcZ/R6j42tfj2Mcsl25TXWX42vfcIixnk6fzv0NMFPSDElbAXOARR0u02xL53Zl1ic6eiQaERslfQT4V2AUcGFE3NnJMs22dG5XZv2j06dziYgrgCsqXGVTp6h6rN9jdHzt62mMFbcrb+/2Ob72DYcYN6OIze7zMTMzsya42z8zM7OS+jKJSpooabGk5fl1QoP5XpJ0S/5bVBg/Q9LSvPyl+eaLrsYnaV9Jv5Z0p6TbJP1ZYdpFklYVYt+3wtgG7Q5O0pi8TVbkbTS9MO2MPP4eSUdUFVOL8X1S0l15my2RtFthWt393eX4TpC0oRDH+wvT5uY6sVzS3E7E1yrXh67E2NM60UR85xViu1fSE4Vp3WhTF0paL+mOBtMl6Rs5/tsk7V+Y1ndtajMR0Xd/wFeA0/Pw6cCXG8z3TIPxlwFz8vD5wIe6HR/wBmBmHp4CrAPG5/cXAe/swHYbBdwH7A5sBdwK7FUzz4eB8/PwHODSPLxXnn8MMCOvZ1QP4vuvwLg8/KGB+Abb312O7wTg7+osOxFYmV8n5OEJnYzX9aGz9WE41Ilm4quZ/1TSjWjd3IZvBfYH7mgw/Wjg56T/fz4QWNqt7VfFX18eiZK6MJufh+cDxza7oCQBhwILyyzfpCHji4h7I2J5Hl4LrAcmVRxHrWa6gyvGvhA4LG+z2cAlEfF8RKwCVuT1dTW+iLgqIp7Lb28g/Q9kt7TTnd4RwOKIeCwiHgcWA0d2KM5muT50IcZBdKNOtBrf8cCCimMYVERcCzw2yCyzgYsjuQEYL2ky/dmmNtOvSXTniFgHkF93ajDf1pKWSbpB0kAi2wF4IiI25ved6BKt2fgAkHQA6VfifYXR5+RTF+dJGlNRXM10B/fyPHkbPUnaZt3oSq7VMuaRfqEOqLe/exHf/8z7bqGkgU4P+rErPteH9vV7nWi6jHwqfAZwZWF0N7bhUBp9hn5sU5vp+L+4NCLpl8Br60w6s4XV7BoRayXtDlwp6XbgqTrztXwLckXxkX9R/QCYGxH/kUefATxESqwXAKcBX2w1xnrF1RlX+9kbzdNUF41taroMSe8GZgEHF0Zvtr8j4r56y3cwvn8EFkTE85JOJh3FHdrkst3m+tCdGHtZJ1opYw6wMCJeKozrxjYcSi/rYNt6diQaEYdHxD51/i4HHs7JZyAJrW+wjrX5dSVwNbAfqe/F8ZIGfiDU7WqwG/FJ2h74Z+Bz+TTFwLrX5VMXzwPfp7rTZM10s/jyPHkbvYZ0qqWpLhq7EB+SDif9WHlH3kZAw/3d1fgi4tFCTN8B/nOzy/aA60MXYuxxnWiljDnUnMrt0jYcSqPP0I9tanO9vihb7w/4Kza9cecrdeaZAIzJwzsCy8kX1IGfsOmNRR/uQXxbAUuAj9eZNjm/CvgacG5FcY0mXXyfwSs3GexdM88pbHojyWV5eG82vZFkJdXfSNJMfPuRTnvPbHZ/dzm+yYXh/wHckIcnAqtynBPy8MQq43N96G59GA51opn48nx7APeT+wbo5jbM659O4xuL3s6mNxbd2K3tV8ln63UADTbqDqQEtDy/TszjZwHfzcMHAbfnSnM7MK+w/O7AjaSbIX4yUFG6HN+7gReBWwp/++ZpV+aY7wB+CGxbYWxHA/fmL54z87gvkn7FA2ydt8mKvI12Lyx7Zl7uHuCoDu3boeL7JfBwYZstGmp/dzm+LwF35jiuAvYsLPu+vF1XACf2sg25PoycOjFUfPn956n5sd7FNrWA9N8JL5KOLucBJwMn5+kiPWT+vhzHrG5uv3b/3GORmZlZSf16d66ZmVnfcxI1MzMryUnUzMysJCdRMzOzkpxEzczMSnISNTMzK8lJ1MzMrCQnUTMzs5L+PxnwwDYSn69tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22872250710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2286fe4f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2287005be48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for c in high_error.columns:\n",
    "    plt.figure(figsize=(7,3))\n",
    "    plt.subplot(121)\n",
    "    plt.hist(low_error[c], color='b')\n",
    "    plt.title(c + ' low_error')\n",
    "    plt.subplot(122)\n",
    "    plt.hist(high_error[c], color='r')\n",
    "    plt.title(c + ' high_error')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is how to do Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pf = PolynomialFeatures(degree=2)\n",
    "pf.fit(X_train_scaled)\n",
    "X_train_scaled = pf.transform(X_train_scaled)\n",
    "X_test_scaled = pf.transform(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression mrse_train: 17.37364523350725, mrse_test: 83322844964323.75\n",
      "Ridge mrse_train: 27.603488386850938, mrse_test: 36.85497933721431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jamheras\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso mrse_train: 24.765015082084858, mrse_test: 33.81992155696889\n"
     ]
    }
   ],
   "source": [
    "for model, name in zip([lr, ridge, lasso], ['LinearRegression', 'Ridge', 'Lasso']):\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    y_pred_train = model.predict(X_train_scaled)\n",
    "    mrse_train = np.sqrt(mean_squared_error(y_pred=y_pred_train, y_true=y_train))\n",
    "    y_pred = model.predict(X_test_scaled)\n",
    "    mrse_test = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_test))\n",
    "    print(name + ' mrse_train: ' + str(mrse_train) + ', mrse_test: ' + str(mrse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_features = pf.get_feature_names(X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "room_type_Entire home/apt, 17.06857333561453\n",
      "accommodates, 5.508669129276822\n",
      "accommodates room_type_Entire home/apt, 5.474484002258369\n",
      "bedrooms room_type_Entire home/apt, 4.650325284770139\n",
      "overall_satisfaction, 4.021397330011996\n",
      "bedrooms neighborhood_Westend-Nord, 3.6987815050080894\n",
      "bedrooms neighborhood_Gallus, 3.3484602781944686\n",
      "accommodates neighborhood_Westend-Nord, 2.9428760125511717\n",
      "room_type_Entire home/apt neighborhood_Gutleutviertel, 2.809364756009057\n",
      "bedrooms neighborhood_Nordend-West, 2.336028534058872\n",
      "neighborhood_Gallus^2, 2.1404072299212036\n",
      "overall_satisfaction accommodates, 2.1310895877567906\n",
      "room_type_Entire home/apt neighborhood_Gallus, 1.7660959261257292\n",
      "overall_satisfaction neighborhood_Bockenheim, 1.3588986284299083\n",
      "overall_satisfaction neighborhood_Gutleutviertel, 1.3334540203128757\n",
      "room_type_Shared room neighborhood_Innenstadt, 1.2032801540702356\n",
      "overall_satisfaction neighborhood_Sachsenhausen-S., 1.1771090218012124\n",
      "overall_satisfaction neighborhood_Westend-Nord, 0.816966123605053\n",
      "room_type_Entire home/apt neighborhood_Westend-Nord, 0.6916879806192954\n",
      "bedrooms neighborhood_Sachsenhausen-N., 0.658714726053261\n",
      "neighborhood_Westend-Nord^2, 0.6539727425304839\n",
      "neighborhood_Gutleutviertel^2, 0.6195827132892281\n",
      "bedrooms^2, 0.6052379715312881\n",
      "neighborhood_Westend-Süd^2, 0.4583636148294691\n",
      "bedrooms bedrooms_per_accommodates, 0.4539596291055682\n",
      "room_type_Private room neighborhood_Niederrad, 0.3034226685962462\n",
      "logreviews neighborhood_Niederrad, 0.2929722928954294\n",
      "room_type_Shared room neighborhood_Ostend, 0.290178266474876\n",
      "bedrooms neighborhood_Nordend-Ost, 0.2454435208877271\n",
      "room_type_Entire home/apt neighborhood_Dornbusch, 0.19961918717948102\n",
      "room_type_Private room neighborhood_Bornheim, 0.1566643244202211\n",
      "neighborhood_Fechenheim^2, 0.0993249575168955\n",
      "neighborhood_Nied^2, 0.06971922663146245\n",
      "neighborhood_Bockenheim^2, 0.04506550120251817\n",
      "neighborhood_Sossenheim^2, 0.03479493419248732\n",
      "neighborhood_Dornbusch^2, 0.013232972419017913\n"
     ]
    }
   ],
   "source": [
    "order = np.argsort(np.abs(lasso.coef_))[::-1]\n",
    "for i in order:\n",
    "    coef_ = lasso.coef_[i]\n",
    "    if coef_ > 0:\n",
    "        print(poly_features[i] + ', ' + str(lasso.coef_[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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